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electroencephalo-gram</title> <meta name="description" content="Search results for: electroencephalo-gram"> <meta name="keywords" content="electroencephalo-gram"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="electroencephalo-gram"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 74</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: electroencephalo-gram</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">74</span> The Effect of Mental Workload Towards Mental Fatigue on Customer Care Agent Using Electroencephalogram</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maya%20Arlini%20Puspasari">Maya Arlini Puspasari</a>, <a href="https://publications.waset.org/abstracts/search?q=Shafira%20Karamina%20Alifah"> Shafira Karamina Alifah</a>, <a href="https://publications.waset.org/abstracts/search?q=Hardianto%20Iridiastadi"> Hardianto Iridiastadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> High mental workload can lead to fatigue and further result in decreased concentration and work performance. This study is conducted to see the effects of mental workload towards mental fatigue. Mental fatigue measurement was conducted at the first and the last 10 minutes of the working time using electroencephalogram, while mental workload measurement was conducted after the work is completed using the NASA-TLX questionnaire. The result shows that there is an increase in alpha band which indicates an increase in mental fatigue. This study also shows absolute alpha is more sensitive compared to the relative alpha. This study proves that there is a relationship between mental workload and mental fatigue although not relatively strong. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mental%20workload" title="mental workload">mental workload</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20care%20agents" title=" customer care agents"> customer care agents</a>, <a href="https://publications.waset.org/abstracts/search?q=NASA-TLX" title=" NASA-TLX"> NASA-TLX</a> </p> <a href="https://publications.waset.org/abstracts/55448/the-effect-of-mental-workload-towards-mental-fatigue-on-customer-care-agent-using-electroencephalogram" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55448.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">222</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">73</span> Fast and Accurate Model to Detect Ictal Waveforms in Electroencephalogram Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Piyush%20Swami">Piyush Swami</a>, <a href="https://publications.waset.org/abstracts/search?q=Bijaya%20Ketan%20Panigrahi"> Bijaya Ketan Panigrahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sneh%20Anand"> Sneh Anand</a>, <a href="https://publications.waset.org/abstracts/search?q=Manvir%20Bhatia"> Manvir Bhatia</a>, <a href="https://publications.waset.org/abstracts/search?q=Tapan%20Gandhi"> Tapan Gandhi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Visual inspection of electroencephalogram (EEG) signals to detect epileptic signals is very challenging and time-consuming task even for any expert neurophysiologist. This problem is most challenging in under-developed and developing countries due to shortage of skilled neurophysiologists. In the past, notable research efforts have gone in trying to automate the seizure detection process. However, due to high false alarm detections and complexity of the models developed so far, have vastly delimited their practical implementation. In this paper, we present a novel scheme for epileptic seizure detection using empirical mode decomposition technique. The intrinsic mode functions obtained were then used to calculate the standard deviations. This was followed by probability density based classifier to discriminate between non-ictal and ictal patterns in EEG signals. The model presented here demonstrated very high classification rates ( > 97%) without compromising the statistical performance. The computation timings for each testing phase were also very low ( < 0.029 s) which makes this model ideal for practical applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram%20%28EEG%29" title="electroencephalogram (EEG)">electroencephalogram (EEG)</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=ictal%20patterns" title=" ictal patterns"> ictal patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title=" empirical mode decomposition"> empirical mode decomposition</a> </p> <a href="https://publications.waset.org/abstracts/64484/fast-and-accurate-model-to-detect-ictal-waveforms-in-electroencephalogram-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64484.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">406</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">72</span> Electroencephalogram Based Approach for Mental Stress Detection during Gameplay with Level Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Priyadarsini%20Samal">Priyadarsini Samal</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajesh%20Singla"> Rajesh Singla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many mobile games come with the benefits of entertainment by introducing stress to the human brain. In recognizing this mental stress, the brain-computer interface (BCI) plays an important role. It has various neuroimaging approaches which help in analyzing the brain signals. Electroencephalogram (EEG) is the most commonly used method among them as it is non-invasive, portable, and economical. Here, this paper investigates the pattern in brain signals when introduced with mental stress. Two healthy volunteers played a game whose aim was to search hidden words from the grid, and the levels were chosen randomly. The EEG signals during gameplay were recorded to investigate the impacts of stress with the changing levels from easy to medium to hard. A total of 16 features of EEG were analyzed for this experiment which includes power band features with relative powers, event-related desynchronization, along statistical features. Support vector machine was used as the classifier, which resulted in an accuracy of 93.9% for three-level stress analysis; for two levels, the accuracy of 92% and 98% are achieved. In addition to that, another game that was similar in nature was played by the volunteers. A suitable regression model was designed for prediction where the feature sets of the first and second game were used for testing and training purposes, respectively, and an accuracy of 73% was found. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20computer%20interface" title="brain computer interface">brain computer interface</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20model" title=" regression model"> regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=stress" title=" stress"> stress</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20search" title=" word search"> word search</a> </p> <a href="https://publications.waset.org/abstracts/139736/electroencephalogram-based-approach-for-mental-stress-detection-during-gameplay-with-level-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139736.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">187</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">71</span> Detecting Music Enjoyment Level Using Electroencephalogram Signals and Machine Learning Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raymond%20Feng">Raymond Feng</a>, <a href="https://publications.waset.org/abstracts/search?q=Shadi%20Ghiasi"> Shadi Ghiasi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=mood" title=" mood"> mood</a>, <a href="https://publications.waset.org/abstracts/search?q=music%20enjoyment" title=" music enjoyment"> music enjoyment</a>, <a href="https://publications.waset.org/abstracts/search?q=physiological%20signals" title=" physiological signals"> physiological signals</a> </p> <a href="https://publications.waset.org/abstracts/182307/detecting-music-enjoyment-level-using-electroencephalogram-signals-and-machine-learning-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182307.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">61</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">70</span> From Electroencephalogram to Epileptic Seizures Detection by Using Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaetano%20Zazzaro">Gaetano Zazzaro</a>, <a href="https://publications.waset.org/abstracts/search?q=Angelo%20Martone"> Angelo Martone</a>, <a href="https://publications.waset.org/abstracts/search?q=Roberto%20V.%20Montaquila"> Roberto V. Montaquila</a>, <a href="https://publications.waset.org/abstracts/search?q=Luigi%20Pavone"> Luigi Pavone</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure%20detection" title=" seizure detection"> seizure detection</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a> </p> <a href="https://publications.waset.org/abstracts/109326/from-electroencephalogram-to-epileptic-seizures-detection-by-using-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109326.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">188</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">69</span> Electroencephalogram Signals Controlling a Parallax Boe-Bot Robot </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nema%20M.%20Salem">Nema M. Salem</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanan%20A.%20Altukhaifi"> Hanan A. Altukhaifi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amal%20Mukhtar"> Amal Mukhtar</a>, <a href="https://publications.waset.org/abstracts/search?q=Reemaz%20K.%20Hetaimish"> Reemaz K. Hetaimish</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, BCI field of research has gained a lot of interest. Apart from motor neuroprosthetics, many studies showed the possibility of controlling a virtual environment of a videogame using the acquired electroencephalogram signals (EEG) from the gamer. In addition, another study had successfully moved a farm tractor using the human’s EEG signals. This article utilizes the use of EEG signals, as a source of technology, in controlling a Parallax Boe-Bot robot. The commercial Emotive Epoc headset has been used in acquiring the EEG signals from rested subjects. Because the human's visual cortex can successfully differentiate between different colors, the red and green colors are used as visual stimuli for generating EEG signals using the Epoc. Arduino and Labview are used to translate the virtually pressed keys into instructions controlling the motion and rotation of the robot. Optimistic results have been achieved except for minor delay and accuracy in the robot’s response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BCI" title="BCI">BCI</a>, <a href="https://publications.waset.org/abstracts/search?q=Emotiv%20Epoc%20headset" title=" Emotiv Epoc headset"> Emotiv Epoc headset</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=Labview" title=" Labview"> Labview</a>, <a href="https://publications.waset.org/abstracts/search?q=Arduino%20applications" title=" Arduino applications"> Arduino applications</a>, <a href="https://publications.waset.org/abstracts/search?q=robot" title=" robot"> robot</a> </p> <a href="https://publications.waset.org/abstracts/19505/electroencephalogram-signals-controlling-a-parallax-boe-bot-robot" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19505.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">522</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">68</span> Automatic Seizure Detection Using Weighted Permutation Entropy and Support Vector Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noha%20Seddik">Noha Seddik</a>, <a href="https://publications.waset.org/abstracts/search?q=Sherine%20Youssef"> Sherine Youssef</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Kholeif"> Mohamed Kholeif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The automated epileptic seizure detection research field has emerged in the recent years; this involves analyzing the Electroencephalogram (EEG) signals instead of the traditional visual inspection performed by expert neurologists. In this study, a Support Vector Machine (SVM) that uses Weighted Permutation Entropy (WPE) as the input feature is proposed for classifying normal and seizure EEG records. WPE is a modified statistical parameter of the permutation entropy (PE) that measures the complexity and irregularity of a time series. It incorporates both the mapped ordinal pattern of the time series and the information contained in the amplitude of its sample points. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram%20%28EEG%29" title="electroencephalogram (EEG)">electroencephalogram (EEG)</a>, <a href="https://publications.waset.org/abstracts/search?q=epileptic%20seizure%20detection" title=" epileptic seizure detection"> epileptic seizure detection</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20permutation%20entropy%20%28WPE%29" title=" weighted permutation entropy (WPE)"> weighted permutation entropy (WPE)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20%28SVM%29" title=" support vector machine (SVM)"> support vector machine (SVM)</a> </p> <a href="https://publications.waset.org/abstracts/12444/automatic-seizure-detection-using-weighted-permutation-entropy-and-support-vector-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12444.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">370</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">67</span> Analysis of Matching Pursuit Features of EEG Signal for Mental Tasks Classification </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zin%20Mar%20Lwin">Zin Mar Lwin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain Computer Interface (BCI) Systems have developed for people who suffer from severe motor disabilities and challenging to communicate with their environment. BCI allows them for communication by a non-muscular way. For communication between human and computer, BCI uses a type of signal called Electroencephalogram (EEG) signal which is recorded from the human„s brain by means of an electrode. The electroencephalogram (EEG) signal is an important information source for knowing brain processes for the non-invasive BCI. Translating human‟s thought, it needs to classify acquired EEG signal accurately. This paper proposed a typical EEG signal classification system which experiments the Dataset from “Purdue University.” Independent Component Analysis (ICA) method via EEGLab Tools for removing artifacts which are caused by eye blinks. For features extraction, the Time and Frequency features of non-stationary EEG signals are extracted by Matching Pursuit (MP) algorithm. The classification of one of five mental tasks is performed by Multi_Class Support Vector Machine (SVM). For SVMs, the comparisons have been carried out for both 1-against-1 and 1-against-all methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BCI" title="BCI">BCI</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=ICA" title=" ICA"> ICA</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a> </p> <a href="https://publications.waset.org/abstracts/19307/analysis-of-matching-pursuit-features-of-eeg-signal-for-mental-tasks-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19307.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">277</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">66</span> Multiscale Entropy Analysis of Electroencephalogram (EEG) of Alcoholic and Control Subjects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lal%20Hussain">Lal Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Wajid%20Aziz"> Wajid Aziz</a>, <a href="https://publications.waset.org/abstracts/search?q=Imtiaz%20Ahmed%20Awan"> Imtiaz Ahmed Awan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharjeel%20Saeed"> Sharjeel Saeed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multiscale entropy analysis (MSE) is a useful technique recently developed to quantify the dynamics of physiological signals at different time scales. This study is aimed at investigating the electroencephalogram (EEG) signals to analyze the background activity of alcoholic and control subjects by inspecting various coarse-grained sequences formed at different time scales. EEG recordings of alcoholic and control subjects were taken from the publically available machine learning repository of University of California (UCI) acquired using 64 electrodes. The MSE analysis was performed on the EEG data acquired from all the electrodes of alcoholic and control subjects. Mann-Whitney rank test was used to find significant differences between the groups and result were considered statistically significant for p-values<0.05. The area under receiver operator curve was computed to find the degree separation between the groups. The mean ranks of MSE values at all the times scales for all electrodes were higher control subject as compared to alcoholic subjects. Higher mean ranks represent higher complexity and vice versa. The finding indicated that EEG signals acquired through electrodes C3, C4, F3, F7, F8, O1, O2, P3, T7 showed significant differences between alcoholic and control subjects at time scales 1 to 5. Moreover, all electrodes exhibit significance level at different time scales. Likewise, the highest accuracy and separation was obtained at the central region (C3 and C4), front polar regions (P3, O1, F3, F7, F8 and T8) while other electrodes such asFp1, Fp2, P4 and F4 shows no significant results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram%20%28EEG%29" title="electroencephalogram (EEG)">electroencephalogram (EEG)</a>, <a href="https://publications.waset.org/abstracts/search?q=multiscale%20sample%20entropy%20%28MSE%29" title=" multiscale sample entropy (MSE)"> multiscale sample entropy (MSE)</a>, <a href="https://publications.waset.org/abstracts/search?q=Mann-Whitney%20test%20%28MMT%29" title=" Mann-Whitney test (MMT)"> Mann-Whitney test (MMT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Receiver%20Operator%20Curve%20%28ROC%29" title=" Receiver Operator Curve (ROC)"> Receiver Operator Curve (ROC)</a>, <a href="https://publications.waset.org/abstracts/search?q=complexity%20analysis" title=" complexity analysis"> complexity analysis</a> </p> <a href="https://publications.waset.org/abstracts/11282/multiscale-entropy-analysis-of-electroencephalogram-eeg-of-alcoholic-and-control-subjects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11282.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">376</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">65</span> IoT Based Approach to Healthcare System for a Quadriplegic Patient Using EEG</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Gautam">R. Gautam</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Sastha%20Kanagasabai"> P. Sastha Kanagasabai</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20N.%20Rathna"> G. N. Rathna </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The proposed healthcare system enables quadriplegic patients, people with severe motor disabilities to send commands to electronic devices and monitor their vitals. The growth of Brain-Computer-Interface (BCI) has led to rapid development in 'assistive systems' for the disabled called 'assistive domotics'. Brain-Computer-Interface is capable of reading the brainwaves of an individual and analyse it to obtain some meaningful data. This processed data can be used to assist people having speech disorders and sometimes people with limited locomotion to communicate. In this Project, Emotiv EPOC Headset is used to obtain the electroencephalogram (EEG). The obtained data is processed to communicate pre-defined commands over the internet to the desired mobile phone user. Other Vital Information like the heartbeat, blood pressure, ECG and body temperature are monitored and uploaded to the server. Data analytics enables physicians to scan databases for a specific illness. The Data is processed in Intel Edison, system on chip (SoC). Patient metrics are displayed via Intel IoT Analytics cloud service. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20computer%20interface" title="brain computer interface">brain computer interface</a>, <a href="https://publications.waset.org/abstracts/search?q=Intel%20Edison" title=" Intel Edison"> Intel Edison</a>, <a href="https://publications.waset.org/abstracts/search?q=Emotiv%20EPOC" title=" Emotiv EPOC"> Emotiv EPOC</a>, <a href="https://publications.waset.org/abstracts/search?q=IoT%20analytics" title=" IoT analytics"> IoT analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a> </p> <a href="https://publications.waset.org/abstracts/57525/iot-based-approach-to-healthcare-system-for-a-quadriplegic-patient-using-eeg" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57525.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">186</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">64</span> Deep Learning Approaches for Accurate Detection of Epileptic Seizures from Electroencephalogram Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramzi%20Rihane">Ramzi Rihane</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassine%20Benayed"> Yassine Benayed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Timely and accurate detection of these seizures is essential for improving patient care. In this study, we leverage the UK Bonn University open-source EEG dataset and employ advanced deep-learning techniques to automate the detection of epileptic seizures. By extracting key features from both time and frequency domains, as well as Spectrogram features, we enhance the performance of various deep learning models. Our investigation includes architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), 1D Convolutional Neural Networks (1D-CNN), and hybrid CNN-LSTM and CNN-BiLSTM models. The models achieved impressive accuracies: LSTM (98.52%), Bi-LSTM (98.61%), CNN-LSTM (98.91%), CNN-BiLSTM (98.83%), and CNN (98.73%). Additionally, we utilized a data augmentation technique called SMOTE, which yielded the following results: CNN (97.36%), LSTM (97.01%), Bi-LSTM (97.23%), CNN-LSTM (97.45%), and CNN-BiLSTM (97.34%). These findings demonstrate the effectiveness of deep learning in capturing complex patterns in EEG signals, providing a reliable and scalable solution for real-time seizure detection in clinical environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title="electroencephalogram">electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=epileptic%20seizure" title=" epileptic seizure"> epileptic seizure</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=BI-LSTM" title=" BI-LSTM"> BI-LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure%20detection" title=" seizure detection"> seizure detection</a> </p> <a href="https://publications.waset.org/abstracts/193110/deep-learning-approaches-for-accurate-detection-of-epileptic-seizures-from-electroencephalogram-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193110.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">12</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">63</span> Attentional Engagement for Movie</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wuon-Shik%20Kim">Wuon-Shik Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyoung-Min%20Choi"> Hyoung-Min Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeonggeon%20Woo"> Jeonggeon Woo</a>, <a href="https://publications.waset.org/abstracts/search?q=Sun%20Jung%20Kwon"> Sun Jung Kwon</a>, <a href="https://publications.waset.org/abstracts/search?q=SeungHee%20Lee"> SeungHee Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research on attentional engagement (AE) in movies using physiological signals is rare and controversial. Therefore, whether physiological responses can be applied to evaluate AE in actual movies is unclear. To clarify this, we measured electrocardiogram and electroencephalogram (EEG) of 16 Japanese university students as they watched the American movie Iron Man. After the viewing, we evaluated the subjective AE and affection levels for 11 film content segments in Iron Man. Based on self-reports for AE, we selected two film content segments as stimuli: Film Content 9 describing Tony Stark (the main character) flying through the night sky (with the highest AE score) and Film Content 1, describing Tony Stark and his colleagues telling indecent jokes (with the lowest score). We divided these two content segments into two time intervals, respectively. Results indicated that the Film Content by Interval interaction for HR was significant, at F (1, 11)=35.64, p<.001, η2=.76; while HR in Film Content 1 decreased, that of in Film Content 9 increased. In Film Content 9, the main effects of the Interval for respiratory sinus arrhythmia (RSA) (F (1, 11)=5.91, p<.05, η2=.35) and for the attention index of EEG (F (1, 11)=5.23, p<.05, η2=.37) were significant. The increase in the RSA was significant (p<.05) as well, whereas that of the EEG attention index was nearly significant (p=.069). In conclusion, while RSA increases, HR decreases when people direct their attention toward normal films. However, while paying attention to a film evoking excitement, HR as well as RSA can increase. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attentional%20engagement" title="attentional engagement">attentional engagement</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=movie" title=" movie"> movie</a>, <a href="https://publications.waset.org/abstracts/search?q=respiratory%20sinus%20arrhythmia" title=" respiratory sinus arrhythmia"> respiratory sinus arrhythmia</a> </p> <a href="https://publications.waset.org/abstracts/45337/attentional-engagement-for-movie" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45337.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">363</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">62</span> Major Depressive Disorder: Diagnosis based on Electroencephalogram Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wajid%20Mumtaz">Wajid Mumtaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Aamir%20Saeed%20Malik"> Aamir Saeed Malik</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Saad%20Azhar%20Ali"> Syed Saad Azhar Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Azhar%20Mohd%20Yasin"> Mohd Azhar Mohd Yasin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a technique based on electroencephalogram (EEG) analysis is presented, aiming for diagnosing major depressive disorder (MDD) among a potential population of MDD patients and healthy controls. EEG is recognized as a clinical modality during applications such as seizure diagnosis, index for anesthesia, detection of brain death or stroke. However, its usability for psychiatric illnesses such as MDD is less studied. Therefore, in this study, for the sake of diagnosis, 2 groups of study participants were recruited, 1) MDD patients, 2) healthy people as controls. EEG data acquired from both groups were analyzed involving inter-hemispheric asymmetry and composite permutation entropy index (CPEI). To automate the process, derived quantities from EEG were utilized as inputs to classifier such as logistic regression (LR) and support vector machine (SVM). The learning of these classification models was tested with a test dataset. Their learning efficiency is provided as accuracy of classifying MDD patients from controls, their sensitivities and specificities were reported, accordingly (LR =81.7 % and SVM =81.5 %). Based on the results, it is concluded that the derived measures are indicators for diagnosing MDD from a potential population of normal controls. In addition, the results motivate further exploring other measures for the same purpose. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=major%20depressive%20disorder" title="major depressive disorder">major depressive disorder</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis%20based%20on%20EEG" title=" diagnosis based on EEG"> diagnosis based on EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG%20derived%20features" title=" EEG derived features"> EEG derived features</a>, <a href="https://publications.waset.org/abstracts/search?q=CPEI" title=" CPEI"> CPEI</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-hemispheric%20asymmetry" title=" inter-hemispheric asymmetry"> inter-hemispheric asymmetry</a> </p> <a href="https://publications.waset.org/abstracts/22303/major-depressive-disorder-diagnosis-based-on-electroencephalogram-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22303.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">546</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">61</span> Effect of Treadmill Exercise on Fluid Intelligence in Early Adults: Electroencephalogram Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ladda%20Leungratanamart">Ladda Leungratanamart</a>, <a href="https://publications.waset.org/abstracts/search?q=Seree%20Chadcham"> Seree Chadcham</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fluid intelligence declines along with age, but it can be developed. For this reason, increasing fluid intelligence in young adults can be possible. This study examined the effects of a two-month treadmill exercise program on fluid intelligence. The researcher designed a treadmill exercise program to promote cardiorespiratory fitness. Thirty-eight healthy voluntary students from the Boromarajonani College of Nursing, Chon Buri were assigned randomly to an exercise group (n=18) and a control group (n=20). The experiment consisted of three sessions: The baseline session consisted of measuring the VO<sub>2</sub>max, electroencephalogram and behavioral response during performed the Raven Progressive Matrices (RPM) test, a measure of fluid intelligence. For the exercise session, an experimental group exercises using treadmill training at 60 % to 80 % maximum heart rate for 30 mins, three times per week, whereas the control group did not exercise. For the following two sessions, each participant was measured the same as baseline testing. The data were analyzed using the t-test to examine whether there is significant difference between the means of the two groups. The results showed that the mean VO<sub>2</sub> max in the experimental group were significantly more than the control group (p<.05), suggesting a two-month treadmill exercise program can improve fluid intelligence. When comparing the behavioral data, it was found that experimental group performed RPM test more accurately and faster than the control group. Neuroelectric data indicated a significant increase in percentages of alpha band ERD (%ERD) at P3 and Pz compared to the pre-exercise condition and the control group. These data suggest that a two-month treadmill exercise program can contribute to the development of cardiorespiratory fitness which influences an increase fluid intelligence. Exercise involved in cortical activation in difference brain areas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=treadmill%20exercise" title="treadmill exercise">treadmill exercise</a>, <a href="https://publications.waset.org/abstracts/search?q=fluid%20intelligence" title=" fluid intelligence"> fluid intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=raven%20progressive%20matrices%20test" title=" raven progressive matrices test"> raven progressive matrices test</a>, <a href="https://publications.waset.org/abstracts/search?q=alpha%20band" title=" alpha band"> alpha band</a> </p> <a href="https://publications.waset.org/abstracts/44295/effect-of-treadmill-exercise-on-fluid-intelligence-in-early-adults-electroencephalogram-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44295.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">350</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">60</span> Macrocephaly-Cutis Marmorata Telangiectatica Congenita Associated with Epilepsy: Case Report</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Atitallah%20Sofien">Atitallah Sofien</a>, <a href="https://publications.waset.org/abstracts/search?q=Bouyahia%20Olfa"> Bouyahia Olfa</a>, <a href="https://publications.waset.org/abstracts/search?q=Krifi%20Farah"> Krifi Farah</a>, <a href="https://publications.waset.org/abstracts/search?q=Missaoui%20Nada"> Missaoui Nada</a>, <a href="https://publications.waset.org/abstracts/search?q=Ben%20Rabeh%20Rania"> Ben Rabeh Rania</a>, <a href="https://publications.waset.org/abstracts/search?q=Yahyaoui%20Salem"> Yahyaoui Salem</a>, <a href="https://publications.waset.org/abstracts/search?q=Mazigh%20Sonia"> Mazigh Sonia</a>, <a href="https://publications.waset.org/abstracts/search?q=Boukthir%20Samir"> Boukthir Samir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Cutis marmorata telangiectatica congenita (CMTC) is a rare cutaneous vascular malformation. It most often appears at birth or during the first days of life. Its origin is still unknown. It associates a livedo with telangiectasias of diffuse or segmental topography. In rare cases, it can be associated with neurological disorders such as macrocephaly and, less frequently, with epilepsy. Methodology: We report a case of an infant with Macrocephaly- Cutis marmorata telangiectatica congenita syndrome associated with epilepsy. Results: This is the case of a one month and 15 days old female infant from a non-consanguineous marriage, admitted for a status epilepticus in the context of apyrexia. Infectious and metabolic causes had been eliminated. Physical examination had shown non-infiltrated and reticular livedoid erythematous patches affecting the left upper limb and atrophic on the back of the left hand. Cerebral magnetic resonance imaging (MRI) showed thin layers of bifrontal, temporal, and left parietal hygromas associated with the widening of the bifrontal subarachnoid spaces. The electroencephalogram showed a well-organized sleep tracing with a single right occipital paroxysmal abnormality. Antiepileptic treatment has been administered with good clinical evolution and regression of the skin lesion and a control electroencephalogram without abnormality. Conclusion: This observation illustrates an association of CMTC with both macrocephaly and epilepsy. This pathology, which is relatively benign and has a good prognosis, generally does not require treatment. However, a detailed examination must be carried out, and a follow-up plan must be put in place for each patient presenting with CMTC, given the risk of association with other abnormalities, which can be potentially serious. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cutis%20marmorata%20telangiectatica%20congenita" title="cutis marmorata telangiectatica congenita">cutis marmorata telangiectatica congenita</a>, <a href="https://publications.waset.org/abstracts/search?q=macrocephaly" title=" macrocephaly"> macrocephaly</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=children" title=" children"> children</a> </p> <a href="https://publications.waset.org/abstracts/175681/macrocephaly-cutis-marmorata-telangiectatica-congenita-associated-with-epilepsy-case-report" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175681.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">60</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">59</span> Selection of Optimal Reduced Feature Sets of Brain Signal Analysis Using Heuristically Optimized Deep Autoencoder</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Souvik%20Phadikar">Souvik Phadikar</a>, <a href="https://publications.waset.org/abstracts/search?q=Nidul%20Sinha"> Nidul Sinha</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajdeep%20Ghosh"> Rajdeep Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In brainwaves research using electroencephalogram (EEG) signals, finding the most relevant and effective feature set for identification of activities in the human brain is a big challenge till today because of the random nature of the signals. The feature extraction method is a key issue to solve this problem. Finding those features that prove to give distinctive pictures for different activities and similar for the same activities is very difficult, especially for the number of activities. The performance of a classifier accuracy depends on this quality of feature set. Further, more number of features result in high computational complexity and less number of features compromise with the lower performance. In this paper, a novel idea of the selection of optimal feature set using a heuristically optimized deep autoencoder is presented. Using various feature extraction methods, a vast number of features are extracted from the EEG signals and fed to the autoencoder deep neural network. The autoencoder encodes the input features into a small set of codes. To avoid the gradient vanish problem and normalization of the dataset, a meta-heuristic search algorithm is used to minimize the mean square error (MSE) between encoder input and decoder output. To reduce the feature set into a smaller one, 4 hidden layers are considered in the autoencoder network; hence it is called Heuristically Optimized Deep Autoencoder (HO-DAE). In this method, no features are rejected; all the features are combined into the response of responses of the hidden layer. The results reveal that higher accuracy can be achieved using optimal reduced features. The proposed HO-DAE is also compared with the regular autoencoder to test the performance of both. The performance of the proposed method is validated and compared with the other two methods recently reported in the literature, which reveals that the proposed method is far better than the other two methods in terms of classification accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autoencoder" title="autoencoder">autoencoder</a>, <a href="https://publications.waset.org/abstracts/search?q=brainwave%20signal%20analysis" title=" brainwave signal analysis"> brainwave signal analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/118906/selection-of-optimal-reduced-feature-sets-of-brain-signal-analysis-using-heuristically-optimized-deep-autoencoder" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118906.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">114</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">58</span> Stroke Rehabilitation via Electroencephalogram Sensors and an Articulated Robot</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Winncy%20Du">Winncy Du</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeremy%20Nguyen"> Jeremy Nguyen</a>, <a href="https://publications.waset.org/abstracts/search?q=Harpinder%20Dhillon"> Harpinder Dhillon</a>, <a href="https://publications.waset.org/abstracts/search?q=Reinardus%20Justin%20Halim"> Reinardus Justin Halim</a>, <a href="https://publications.waset.org/abstracts/search?q=Clayton%20Haske"> Clayton Haske</a>, <a href="https://publications.waset.org/abstracts/search?q=Trent%20Hughes"> Trent Hughes</a>, <a href="https://publications.waset.org/abstracts/search?q=Marissa%20Ortiz"> Marissa Ortiz</a>, <a href="https://publications.waset.org/abstracts/search?q=Rozy%20Saini"> Rozy Saini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stroke often causes death or cerebro-vascular (CV) brain damage. Most patients with CV brain damage lost their motor control on their limbs. This paper focuses on developing a reliable, safe, and non-invasive EEG-based robot-assistant stroke rehabilitation system to help stroke survivors to rapidly restore their motor control functions for their limbs. An electroencephalogram (EEG) recording device (EPOC Headset) and was used to detect a patient’s brain activities. The EEG signals were then processed, classified, and interpreted to the motion intentions, and then converted to a series of robot motion commands. A six-axis articulated robot (AdeptSix 300) was employed to provide the intended motions based on these commends. To ensure the EEG device, the computer, and the robot can communicate to each other, an Arduino microcontroller is used to physically execute the programming codes to a series output pins’ status (HIGH or LOW). Then these “hardware” commends were sent to a 24 V relay to trigger the robot’s motion. A lookup table for various motion intensions and the associated EEG signal patterns were created (through training) and installed in the microcontroller. Thus, the motion intention can be direct determined by comparing the EEG patterns obtaibed from the patient with the look-up table’s EEG patterns; and the corresponding motion commends are sent to the robot to provide the intended motion without going through feature extraction and interpretation each time (a time-consuming process). For safety sake, an extender was designed and attached to the robot’s end effector to ensure the patient is beyond the robot’s workspace. The gripper is also designed to hold the patient’s limb. The test results of this rehabilitation system show that it can accurately interpret the patient’s motion intension and move the patient’s arm to the intended position. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20waves" title="brain waves">brain waves</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG%20sensor" title=" EEG sensor"> EEG sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20control" title=" motion control"> motion control</a>, <a href="https://publications.waset.org/abstracts/search?q=robot-assistant%20stroke%20rehabilitation" title=" robot-assistant stroke rehabilitation"> robot-assistant stroke rehabilitation</a> </p> <a href="https://publications.waset.org/abstracts/63586/stroke-rehabilitation-via-electroencephalogram-sensors-and-an-articulated-robot" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63586.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">383</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">57</span> Signals Monitored during Anaesthesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Launcelot.McGrath">Launcelot.McGrath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Biosignal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. Understanding which biological signals are most important during anaesthesia is critically important. It is important that the anaesthesiologist understand both the signals themselves and the limitations introduced by the processes of acquisition. In this article, we provide an overview of different types of biological signals as well as the mechanisms applied to acquire them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=general%20biosignals" title="general biosignals">general biosignals</a>, <a href="https://publications.waset.org/abstracts/search?q=anaesthesia" title=" anaesthesia"> anaesthesia</a>, <a href="https://publications.waset.org/abstracts/search?q=biological" title=" biological"> biological</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a> </p> <a href="https://publications.waset.org/abstracts/158537/signals-monitored-during-anaesthesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158537.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">56</span> Electroencephalogram during Natural Reading: Theta and Alpha Rhythms as Analytical Tools for Assessing a Reader’s Cognitive State</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20Zhigulskaya">D. Zhigulskaya</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Anisimov"> V. Anisimov</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Pikunov"> A. Pikunov</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Babanova"> K. Babanova</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Zuev"> S. Zuev</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Latyshkova"> A. Latyshkova</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20%D0%A1hernozatonskiy"> K. Сhernozatonskiy</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Revazov"> A. Revazov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrophysiology of information processing in reading is certainly a popular research topic. Natural reading, however, has been relatively poorly studied, despite having broad potential applications for learning and education. In the current study, we explore the relationship between text categories and spontaneous electroencephalogram (EEG) while reading. Thirty healthy volunteers (mean age 26,68 ± 1,84) participated in this study. 15 Russian-language texts were used as stimuli. The first text was used for practice and was excluded from the final analysis. The remaining 14 were opposite pairs of texts in one of 7 categories, the most important of which were: interesting/boring, fiction/non-fiction, free reading/reading with an instruction, reading a text/reading a pseudo text (consisting of strings of letters that formed meaningless words). Participants had to read the texts sequentially on an Apple iPad Pro. EEG was recorded from 12 electrodes simultaneously with eye movement data via ARKit Technology by Apple. EEG spectral amplitude was analyzed in Fz for theta-band (4-8 Hz) and in C3, C4, P3, and P4 for alpha-band (8-14 Hz) using the Friedman test. We found that reading an interesting text was accompanied by an increase in theta spectral amplitude in Fz compared to reading a boring text (3,87 µV ± 0,12 and 3,67 µV ± 0,11, respectively). When instructions are given for reading, we see less alpha activity than during free reading of the same text (3,34 µV ± 0,20 and 3,73 µV ± 0,28, respectively, for C4 as the most representative channel). The non-fiction text elicited less activity in the alpha band (C4: 3,60 µV ± 0,25) than the fiction text (C4: 3,66 µV ± 0,26). A significant difference in alpha spectral amplitude was also observed between the regular text (C4: 3,64 µV ± 0,29) and the pseudo text (C4: 3,38 µV ± 0,22). These results suggest that some brain activity we see on EEG is sensitive to particular features of the text. We propose that changes in theta and alpha bands during reading may serve as electrophysiological tools for assessing the reader’s cognitive state as well as his or her attitude to the text and the perceived information. These physiological markers have prospective practical value for developing technological solutions and biofeedback systems for reading in particular and for education in general. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20reading" title=" natural reading"> natural reading</a>, <a href="https://publications.waset.org/abstracts/search?q=reader%27s%20cognitive%20state" title=" reader's cognitive state"> reader's cognitive state</a>, <a href="https://publications.waset.org/abstracts/search?q=theta-rhythm" title=" theta-rhythm"> theta-rhythm</a>, <a href="https://publications.waset.org/abstracts/search?q=alpha-rhythm" title=" alpha-rhythm"> alpha-rhythm</a> </p> <a href="https://publications.waset.org/abstracts/154843/electroencephalogram-during-natural-reading-theta-and-alpha-rhythms-as-analytical-tools-for-assessing-a-readers-cognitive-state" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154843.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">80</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">55</span> Intrinsic Motivational Factor of Students in Learning Mathematics and Science Based on Electroencephalogram Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Norzaliza%20Md.%20Nor">Norzaliza Md. Nor</a>, <a href="https://publications.waset.org/abstracts/search?q=Sh-Hussain%20Salleh"> Sh-Hussain Salleh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahyar%20Hamedi"> Mahyar Hamedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadrina%20Hussain"> Hadrina Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Wahab%20Abdul%20Rahman"> Wahab Abdul Rahman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Motivational factor is mainly the students’ desire to involve in learning process. However, it also depends on the goal towards their involvement or non-involvement in academic activity. Even though, the students’ motivation might be in the same level, but the basis of their motivation may differ. In this study, it focuses on the intrinsic motivational factor which student enjoy learning or feeling of accomplishment the activity or study for its own sake. The intrinsic motivational factor of students in learning mathematics and science has found as difficult to be achieved because it depends on students’ interest. In the Program for International Student Assessment (PISA) for mathematics and science, Malaysia is ranked as third lowest. The main problem in Malaysian educational system, students tend to have extrinsic motivation which they have to score in exam in order to achieve a good result and enrolled as university students. The use of electroencephalogram (EEG) signals has found to be scarce especially to identify the students’ intrinsic motivational factor in learning science and mathematics. In this research study, we are identifying the correlation between precursor emotion and its dynamic emotion to verify the intrinsic motivational factor of students in learning mathematics and science. The 2-D Affective Space Model (ASM) was used in this research in order to identify the relationship of precursor emotion and its dynamic emotion based on the four basic emotions, happy, calm, fear and sad. These four basic emotions are required to be used as reference stimuli. Then, in order to capture the brain waves, EEG device was used, while Mel Frequency Cepstral Coefficient (MFCC) was adopted to be used for extracting the features before it will be feed to Multilayer Perceptron (MLP) to classify the valence and arousal axes for the ASM. The results show that the precursor emotion had an influence the dynamic emotions and it identifies that most students have no interest in mathematics and science according to the negative emotion (sad and fear) appear in the EEG signals. We hope that these results can help us further relate the behavior and intrinsic motivational factor of students towards learning of mathematics and science. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=MLP" title=" MLP"> MLP</a>, <a href="https://publications.waset.org/abstracts/search?q=MFCC" title=" MFCC"> MFCC</a>, <a href="https://publications.waset.org/abstracts/search?q=intrinsic%20motivational%20factor" title=" intrinsic motivational factor"> intrinsic motivational factor</a> </p> <a href="https://publications.waset.org/abstracts/52426/intrinsic-motivational-factor-of-students-in-learning-mathematics-and-science-based-on-electroencephalogram-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52426.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">366</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">54</span> The Comparative Electroencephalogram Study: Children with Autistic Spectrum Disorder and Healthy Children Evaluate Classical Music in Different Ways</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Galina%20Portnova">Galina Portnova</a>, <a href="https://publications.waset.org/abstracts/search?q=Kseniya%20Gladun"> Kseniya Gladun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In our EEG experiment participated 27 children with ASD with the average age of 6.13 years and the average score for CARS 32.41 and 25 healthy children (of 6.35 years). Six types of musical stimulation were presented, included Gluck, Javier-Naida, Kenny G, Chopin and other classic musical compositions. Children with autism showed orientation reaction to the music and give behavioral responses to different types of music, some of them might assess stimulation by scales. The participants were instructed to remain calm. Brain electrical activity was recorded using a 19-channel EEG recording device, 'Encephalan' (Russia, Taganrog). EEG epochs lasting 150 s were analyzed using EEGLab plugin for MatLab (Mathwork Inc.). For EEG analysis we used Fast Fourier Transform (FFT), analyzed Peak alpha frequency (PAF), correlation dimension D2 and Stability of rhythms. To express the dynamics of desynchronizing of different rhythms we've calculated the envelope of the EEG signal, using the whole frequency range and a set of small narrowband filters using Hilbert transformation. Our data showed that healthy children showed similar EEG spectral changes during musical stimulation as well as described the feelings induced by musical fragments. The exception was the ‘Chopin. Prelude’ fragment (no.6). This musical fragment induced different subjective feeling, behavioral reactions and EEG spectral changes in children with ASD and healthy children. The correlation dimension D2 was significantly lower in autists compared to healthy children during musical stimulation. Hilbert envelope frequency was reduced in all group of subjects during musical compositions 1,3,5,6 compositions compared to the background. During musical fragments 2 and 4 (terrible) lower Hilbert envelope frequency was observed only in children with ASD and correlated with the severity of the disease. Alfa peak frequency was lower compared to the background during this musical composition in healthy children and conversely higher in children with ASD. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram%20%20%28EEG%29" title="electroencephalogram (EEG)">electroencephalogram (EEG)</a>, <a href="https://publications.waset.org/abstracts/search?q=emotional%20perception" title=" emotional perception"> emotional perception</a>, <a href="https://publications.waset.org/abstracts/search?q=ASD" title=" ASD"> ASD</a>, <a href="https://publications.waset.org/abstracts/search?q=musical%20perception" title=" musical perception"> musical perception</a>, <a href="https://publications.waset.org/abstracts/search?q=childhood%20Autism%20rating%20scale%20%20%28CARS%29" title=" childhood Autism rating scale (CARS)"> childhood Autism rating scale (CARS)</a> </p> <a href="https://publications.waset.org/abstracts/62155/the-comparative-electroencephalogram-study-children-with-autistic-spectrum-disorder-and-healthy-children-evaluate-classical-music-in-different-ways" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62155.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">53</span> A Real Time Set Up for Retrieval of Emotional States from Human Neural Responses</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rashima%20Mahajan">Rashima Mahajan</a>, <a href="https://publications.waset.org/abstracts/search?q=Dipali%20Bansal"> Dipali Bansal</a>, <a href="https://publications.waset.org/abstracts/search?q=Shweta%20Singh"> Shweta Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20computer%20interface" title="brain computer interface">brain computer interface</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=EEGLab" title=" EEGLab"> EEGLab</a>, <a href="https://publications.waset.org/abstracts/search?q=BCILab" title=" BCILab"> BCILab</a>, <a href="https://publications.waset.org/abstracts/search?q=emotive" title=" emotive"> emotive</a>, <a href="https://publications.waset.org/abstracts/search?q=emotions" title=" emotions"> emotions</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20features" title=" interval features"> interval features</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20features" title=" spectral features"> spectral features</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20applications" title=" control applications"> control applications</a> </p> <a href="https://publications.waset.org/abstracts/6428/a-real-time-set-up-for-retrieval-of-emotional-states-from-human-neural-responses" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6428.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">317</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">52</span> Analysis of Biomarkers Intractable Epileptogenic Brain Networks with Independent Component Analysis and Deep Learning Algorithms: A Comprehensive Framework for Scalable Seizure Prediction with Unimodal Neuroimaging Data in Pediatric Patients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bliss%20Singhal">Bliss Singhal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intractable%20epilepsy" title="intractable epilepsy">intractable epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure" title=" seizure"> seizure</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram%20channels" title=" electroencephalogram channels"> electroencephalogram channels</a> </p> <a href="https://publications.waset.org/abstracts/164932/analysis-of-biomarkers-intractable-epileptogenic-brain-networks-with-independent-component-analysis-and-deep-learning-algorithms-a-comprehensive-framework-for-scalable-seizure-prediction-with-unimodal-neuroimaging-data-in-pediatric-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164932.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">84</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">51</span> Signals Monitored During Anaesthesia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Launcelot%20McGrath">Launcelot McGrath</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoxiao%20Liu"> Xiaoxiao Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Colin%20Flanagan"> Colin Flanagan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is widely recognised that a comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Bio signal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. There are tremendous biological signals during anaesthesia, and not all of them are important, which to choose to observe is a significant decision. It is important that the anaesthesiologist understand both the signals themselves, and the limitations introduced by the processes of acquisition. In this article, we provide an all-sided overview of different types of biological signals as well as the mechanisms applied to acquire them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=general%20biosignals" title="general biosignals">general biosignals</a>, <a href="https://publications.waset.org/abstracts/search?q=anaesthesia" title=" anaesthesia"> anaesthesia</a>, <a href="https://publications.waset.org/abstracts/search?q=biological" title=" biological"> biological</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a> </p> <a href="https://publications.waset.org/abstracts/157332/signals-monitored-during-anaesthesia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157332.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">105</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">50</span> A Method of Detecting the Difference in Two States of Brain Using Statistical Analysis of EEG Raw Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Digvijaysingh%20S.%20Bana">Digvijaysingh S. Bana</a>, <a href="https://publications.waset.org/abstracts/search?q=Kiran%20R.%20Trivedi"> Kiran R. Trivedi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces various methods for the alpha wave to detect the difference between two states of brain. One healthy subject participated in the experiment. EEG was measured on the forehead above the eye (FP1 Position) with reference and ground electrode are on the ear clip. The data samples are obtained in the form of EEG raw data. The time duration of reading is of one minute. Various test are being performed on the alpha band EEG raw data.The readings are performed in different time duration of the entire day. The statistical analysis is being carried out on the EEG sample data in the form of various tests. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram%28EEG%29" title="electroencephalogram(EEG)">electroencephalogram(EEG)</a>, <a href="https://publications.waset.org/abstracts/search?q=biometrics" title=" biometrics"> biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=authentication" title=" authentication"> authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG%20raw%20data" title=" EEG raw data"> EEG raw data</a> </p> <a href="https://publications.waset.org/abstracts/32552/a-method-of-detecting-the-difference-in-two-states-of-brain-using-statistical-analysis-of-eeg-raw-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32552.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">464</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">49</span> EEG Diagnosis Based on Phase Space with Wavelet Transforms for Epilepsy Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohmmad%20A.%20Obeidat">Mohmmad A. Obeidat</a>, <a href="https://publications.waset.org/abstracts/search?q=Amjed%20Al%20Fahoum"> Amjed Al Fahoum</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayman%20M.%20Mansour"> Ayman M. Mansour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The recognition of an abnormal activity of the brain functionality is a vital issue. To determine the type of the abnormal activity either a brain image or brain signal are usually considered. Imaging localizes the defect within the brain area and relates this area with somebody functionalities. However, some functions may be disturbed without affecting the brain as in epilepsy. In this case, imaging may not provide the symptoms of the problem. A cheaper yet efficient approach that can be utilized to detect abnormal activity is the measurement and analysis of the electroencephalogram (EEG) signals. The main goal of this work is to come up with a new method to facilitate the classification of the abnormal and disorder activities within the brain directly using EEG signal processing, which makes it possible to be applied in an on-line monitoring system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG" title="EEG">EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet" title=" wavelet"> wavelet</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a> </p> <a href="https://publications.waset.org/abstracts/17206/eeg-diagnosis-based-on-phase-space-with-wavelet-transforms-for-epilepsy-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17206.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">538</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">48</span> Experimental Verification of the Relationship between Physiological Indexes and the Presence or Absence of an Operation during E-learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masaki%20Omata">Masaki Omata</a>, <a href="https://publications.waset.org/abstracts/search?q=Shumma%20Hosokawa"> Shumma Hosokawa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An experiment to verify the relationships between physiological indexes of an e-learner and the presence or absence of an operation during e-learning is described. Electroencephalogram (EEG), hemoencephalography (HEG), skin conductance (SC), and blood volume pulse (BVP) values were measured while participants performed experimental learning tasks. The results show that there are significant differences between the SC values when reading with clicking on learning materials and the SC values when reading without clicking, and between the HEG ratio when reading (with and without clicking) and the HEG ratio when resting for four of five participants. We conclude that the SC signals can be used to estimate whether or not a learner is performing an active task and that the HEG ratios can be used to estimate whether a learner is learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=e-learning" title="e-learning">e-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=physiological%20index" title=" physiological index"> physiological index</a>, <a href="https://publications.waset.org/abstracts/search?q=physiological%20signal" title=" physiological signal"> physiological signal</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20of%20learning" title=" state of learning"> state of learning</a> </p> <a href="https://publications.waset.org/abstracts/38266/experimental-verification-of-the-relationship-between-physiological-indexes-and-the-presence-or-absence-of-an-operation-during-e-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38266.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">378</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">47</span> Characterization of 3D-MRP for Analyzing of Brain Balancing Index (BBI) Pattern</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Fuad">N. Fuad</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20N.%20Taib"> M. N. Taib</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Jailani"> R. Jailani</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20E.%20Marwan"> M. E. Marwan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses on power spectral density (PSD) characteristics which are extracted from three-dimensional (3D) electroencephalogram (EEG) models. The EEG signal recording was conducted on 150 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, the values of maximum PSD were extracted as features from the model. These features are analysed using mean relative power (MRP) and different mean relative power (DMRP) technique to observe the pattern among different brain balancing indexes. The results showed that by implementing these techniques, the pattern of brain balancing indexes can be clearly observed. Some patterns are indicates between index 1 to index 5 for left frontal (LF) and right frontal (RF). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20spectral%20density" title="power spectral density">power spectral density</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20EEG%20model" title=" 3D EEG model"> 3D EEG model</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20balancing" title=" brain balancing"> brain balancing</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20relative%20power" title=" mean relative power"> mean relative power</a>, <a href="https://publications.waset.org/abstracts/search?q=different%20mean%20relative%20power" title=" different mean relative power"> different mean relative power</a> </p> <a href="https://publications.waset.org/abstracts/6107/characterization-of-3d-mrp-for-analyzing-of-brain-balancing-index-bbi-pattern" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6107.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">474</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">46</span> Analysis of Epileptic Electroencephalogram Using Detrended Fluctuation and Recurrence Plots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mrinalini%20Ranjan">Mrinalini Ranjan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sudheesh%20Chethil"> Sudheesh Chethil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a common neurological disorder characterised by the recurrence of seizures. Electroencephalogram (EEG) signals are complex biomedical signals which exhibit nonlinear and nonstationary behavior. We use two methods 1) Detrended Fluctuation Analysis (DFA) and 2) Recurrence Plots (RP) to capture this complex behavior of EEG signals. DFA considers fluctuation from local linear trends. Scale invariance of these signals is well captured in the multifractal characterisation using detrended fluctuation analysis (DFA). Analysis of long-range correlations is vital for understanding the dynamics of EEG signals. Correlation properties in the EEG signal are quantified by the calculation of a scaling exponent. We report the existence of two scaling behaviours in the epileptic EEG signals which quantify short and long-range correlations. To illustrate this, we perform DFA on extant ictal (seizure) and interictal (seizure free) datasets of different patients in different channels. We compute the short term and long scaling exponents and report a decrease in short range scaling exponent during seizure as compared to pre-seizure and a subsequent increase during post-seizure period, while the long-term scaling exponent shows an increase during seizure activity. Our calculation of long-term scaling exponent yields a value between 0.5 and 1, thus pointing to power law behaviour of long-range temporal correlations (LRTC). We perform this analysis for multiple channels and report similar behaviour. We find an increase in the long-term scaling exponent during seizure in all channels, which we attribute to an increase in persistent LRTC during seizure. The magnitude of the scaling exponent and its distribution in different channels can help in better identification of areas in brain most affected during seizure activity. The nature of epileptic seizures varies from patient-to-patient. To illustrate this, we report an increase in long-term scaling exponent for some patients which is also complemented by the recurrence plots (RP). RP is a graph that shows the time index of recurrence of a dynamical state. We perform Recurrence Quantitative analysis (RQA) and calculate RQA parameters like diagonal length, entropy, recurrence, determinism, etc. for ictal and interictal datasets. We find that the RQA parameters increase during seizure activity, indicating a transition. We observe that RQA parameters are higher during seizure period as compared to post seizure values, whereas for some patients post seizure values exceeded those during seizure. We attribute this to varying nature of seizure in different patients indicating a different route or mechanism during the transition. Our results can help in better understanding of the characterisation of epileptic EEG signals from a nonlinear analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detrended%20fluctuation" title="detrended fluctuation">detrended fluctuation</a>, <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title=" epilepsy"> epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20range%20correlations" title=" long range correlations"> long range correlations</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrence%20plots" title=" recurrence plots"> recurrence plots</a> </p> <a href="https://publications.waset.org/abstracts/84822/analysis-of-epileptic-electroencephalogram-using-detrended-fluctuation-and-recurrence-plots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84822.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">45</span> Feedforward Neural Network with Backpropagation for Epilepsy Seizure Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Natalia%20%20Espinosa">Natalia Espinosa</a>, <a href="https://publications.waset.org/abstracts/search?q=Arthur%20Amorim"> Arthur Amorim</a>, <a href="https://publications.waset.org/abstracts/search?q=Rudolf%20%20Huebner"> Rudolf Huebner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Artificial%20Neural%20Network%20%28ANN%29" title="Artificial Neural Network (ANN)">Artificial Neural Network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Wavelet%20Transform%20%28DWT%29" title=" Discrete Wavelet Transform (DWT)"> Discrete Wavelet Transform (DWT)</a>, <a href="https://publications.waset.org/abstracts/search?q=Epilepsy%20Detection" title=" Epilepsy Detection "> Epilepsy Detection </a>, <a href="https://publications.waset.org/abstracts/search?q=Seizure." title=" Seizure."> Seizure.</a> </p> <a href="https://publications.waset.org/abstracts/122872/feedforward-neural-network-with-backpropagation-for-epilepsy-seizure-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122872.pdf" target="_blank" class="btn btn-primary 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