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Title: Sentiment analysis using EEG activities for suicidology
Authors: Prasad, Dilip Kumar
Liu, Shijie
Chen, Annabel Shen-Hsing
Quek, Chai
Keywords: Engineering::Computer science and engineering
Social sciences::Psychology
Issue Date: 2018
Source: Prasad, D. K., Liu, S., Chen, A. S. & Quek, C. (2018). Sentiment analysis using EEG activities for suicidology. Expert Systems With Applications, 103, 206-217.
Journal: Expert Systems with Applications
Abstract: This paper investigates the utility of EEG signals in suicidology, particularly for detection of suicidal ideation through data analysis of EEG signals elicit by reading of text notes, which may indicate genuine suicide attempt or a hoax suicide note. The role of emotion, attention, and memory in detection of suicidal ideation through perusal is studied. Also, the discriminatory role of alpha waves and beta waves is studied. Our study provides an important and interesting conclusion that prior experience or conditioned training in detection of suicide notes is not beneficial in discriminating between genuine and hoax suicide notes and that the brain signals are more discriminatory in participants with no prior experience or conditioning. Quantitatively, our results indicate that the beta waves in EEG channels related to memory can provide classification accuracy of more than 70%.
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2018.03.011
Rights: © 2018 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles
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