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Psychological Effect Computation of Courtroom Arguments: A Deep Learning Approach of EEG Signal Data
Computer Science and Information Systems, Tome 21 (2024) no. 4.

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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.
Keywords: Courtroom argument, attorney’ Speech, psychological effect, EEG, CNN-LSTM 
@article{CSIS_2024_21_4_a8,
     author = {Xuan Zhou and Yaming Liu and Baoqian Jiao and Hanzhen Ouyang and Weihui Dai},
     title = {Psychological {Effect} {Computation} of {Courtroom} {Arguments:} {A} {Deep} {Learning} {Approach} of {EEG} {Signal} {Data}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {21},
     number = {4},
     year = {2024},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2024_21_4_a8/}
}
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Xuan Zhou; Yaming Liu; Baoqian Jiao; Hanzhen Ouyang; Weihui Dai. Psychological Effect Computation of Courtroom Arguments: A Deep Learning Approach of EEG Signal Data. Computer Science and Information Systems, Tome 21 (2024) no. 4. http://geodesic.mathdoc.fr/item/CSIS_2024_21_4_a8/