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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <title>ComSIS | Computer Science and Information Systems</title> <link rel="stylesheet" type="text/css" href="res/style1.css" /> </head> <body> <script type="text/javascript" src="res/wz_tooltip.js"></script> <script type="text/javascript" src="res/slide.js"></script> <div id="all"> <div id="header"> <h1>Computer Science and Information Systems</h1> </div>  <div id="main"> <div id="sidebar"> <p>About the journal</p> <ul> <li><a href="index.php">Home page</a></li> <li><a href="contact.php">Contact information</a></li> <li><a href="aims.php">Aims and scope</a></li> <li><a href="indexing.php">Indexing information</a></li> <li><a href="policies.php">Editorial policies</a></li> <li><a href="consortium.php">ComSIS consortium</a></li> <li><a href="boards.php">Journal boards</a></li> <li><a href="managing.php">Managing board</a></li> </ul> <p>For authors</p> <ul> <li><a href="information.php">Information for contributors</a></li> <li><a href="http://ojs.pmf.uns.ac.rs/index.php/comsis">Paper submission</a></li> <li><a href="submission.php">Article submission through OJS</a></li> <li><a href="copyright.php">Copyright transfer form</a></li> <li><a href="download.php">Download section</a></li> </ul> <p>For readers</p> <ul> <li><a href="archive.php?show=lstnew">Forthcoming articles</a></li> <li><a href="archive.php?show=vol2104">Current issue</a></li> <li><a href="archive.php">Archive</a></li> </ul> <p>For reviewers</p> <ul> <li><a href="http://ojs.pmf.uns.ac.rs/index.php/comsis">View and review submissions</a></li> </ul> <p>News</p> <ul> <li><a href="https://www.facebook.com/ComSISJournal/"> <img src="res/fb.png" alt="FB"/> Journal's Facebook page</a></li> <li><a href="cfp.php">Calls for special issues</a></li> <li><a href="notification.php">New issue notification</a></li> </ul> </div>  <div id="content">  <h1 class="title">Psychological Effect Computation of Courtroom Arguments: A Deep Learning Approach of EEG Signal Data</h1><p class="authors">Xuan Zhou<sup>1</sup>, Yaming Liu<sup>2</sup>, Baoqian Jiao<sup>1</sup>, Hanzhen Ouyang<sup>3</sup> and Weihui Dai<sup>3</sup></p><ol><li>Guanghua Law School, Zhejiang University,<br/>Hangzhou 310008, China<br/>{zhoushelley, jiaobaoqian}@ zju.edu.cn</li><li>School of Mechanical & Automotive Engineering, South China University of Technology,<br/>Guangzhou 510641, China<br/>yamingliu1@163.com</li><li>School of Management, Fudan University,<br/>Shanghai 200433, China<br/>{ouyanghanzhen, whdai}@fudan.edu.cn</li></ol><h3>Abstract</h3><p>Previous studies have shown that the attorney’s speeches can exert significant impacts on the cognition and judgment of the jury in court arguments. However, the psychological effects induced by these speeches are intricately tied to subconscious brain states, making them challenging to accurately and comprehensively describe through subjective self-reports. This study aims to explore a neural reaction observation method for psychological effect analysis of the attorney’s speeches in courtroom scenarios. We utilized a corpus of courtroom arguments from legal movies and television series as source material. Participants’ psychological responses to these speeches were monitored using wearable electroencephalography (EEG) devices. Building upon this data, we employed a deep learning model based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to compute attention intensity, cognitive load, and emotional changes. Our test results demonstrate that this approach enables continuous and dynamic computation within courtroom argument contexts, providing a more accurate assessment of attorneys’ language skills.</p><h3>Key words</h3><p>Courtroom argument, attorney’ Speech, psychological effect, EEG, CNN-LSTM</p><h3>Digital Object Identifier (DOI)</h3><p><a href="https://doi.org/10.2298/CSIS240122037Z">https://doi.org/10.2298/CSIS240122037Z</a></p><h3>Publication information</h3><p><a href="/archive.php?show=vol2104">Volume 21, Issue 4 (September 2024)</a><br/>Year of Publication: 2024<br/>ISSN: 2406-1018 (Online)<br/>Publisher: ComSIS Consortium</p><h3>Full text</h3><p><a class="download" href="pdf.php?id=932-2401"><img class="left" src="res/pdf.png" alt="Download"/>Available in PDF<br/><em>Portable Document Format</em></a></p><h3>How to cite</h3><p>Zhou, X., Liu, Y., Jiao, B., Ouyang, H., Dai, W.: Psychological Effect Computation of Courtroom Arguments: A Deep Learning Approach of EEG Signal Data. Computer Science and Information Systems, Vol. 21, No. 4, 1321–1334. (2024), https://doi.org/10.2298/CSIS240122037Z</p>  </div>  </div>  <div id="footer_top"> </div> <div id="footer"> <div class="left">Faculty of Sciences, Trg Dositeja Obradovića 3, 21000 Novi Sad, Serbia, <a href="mailto:comsis@uns.ac.rs">comsis@uns.ac.rs</a></div> <div class="left">Published by ComSIS Consortium under<br/><a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License<br><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/></a></div> <div class="clearer"> </div> </div>  </div>  </body> </html>