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Tytuł:
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Electroencephalography based emotion detection using ensemble classification and asymmetric brain activity.
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Autorzy:
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Gannouni S; Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia. Electronic address: .
Aledaily A; Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Belwafi K; Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Aboalsamh H; Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
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Źródło:
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Journal of affective disorders [J Affect Disord] 2022 Dec 15; Vol. 319, pp. 416-427. Date of Electronic Publication: 2022 Sep 24.
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Typ publikacji:
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Journal Article; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Original Publication: Amsterdam, Elsevier/North-Holland Biomedical Press.
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MeSH Terms:
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Algorithms*
Electroencephalography*/methods
Humans ; Emotions ; Brain/diagnostic imaging
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Contributed Indexing:
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Keywords: Asymmetric brain activity; Channel selection; Electroencephalography (EEG); Emotion recognition; Ensemble classification; Epoch identification; Numerator Group Delay (NGD); Zero-time windowing (ZTW)
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Entry Date(s):
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Date Created: 20220926 Date Completed: 20221115 Latest Revision: 20221209
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Update Code:
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20240105
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DOI:
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10.1016/j.jad.2022.09.054
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PMID:
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36162677
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Over the past decade, emotion detection using rhythmic brain activity has become a critical area of research. The asymmetrical brain activity has garnered the most significant level of research attention due to its implications for the study of emotions, including hemispheric asymmetry or, more generally, asymmetrical brain activity. This study aimed at enhancing the accuracy of emotion detection using Electroencephalography (EEG) brain signals. This happens by identifying electrodes where relevant brain activity changes occur during the emotions and by defining pairs of relevant electrodes having asymmetric brain activities during emotions. Experimental results showed that the proposed method is highly competitive compared with existing studies of multi-class emotion recognition. These results were improved by processing not the whole EEG signals but by focusing on fragments of the signals, called epochs, which represent the instants where the excitation is maximum during emotions. The epochs were extracted using the zero-time windowing method and the numerator group-delay function.
(Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)
