Classification of emotions from EEG signals using time-order representation based on the S-transform and convolutional neural network

Emotions are the most powerful information source to study the cognition, behaviour, and medical conditions of a person. Accurate identification of emotions helps in the development of affective computing, brain–computer interface, medical diagnosis system, etc. Electroencephalogram (EEG) signals ar...

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Bibliographic Details
Published in:	Electronics letters Vol. 56; no. 25; pp. 1359 - 1361
Main Authors:	Khare, S.K, Nishad, A, Upadhyay, A, Bajaj, V
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 10.12.2020
Subjects:	affective computing behaviour conditions brain‐computer interfaces brain–computer interface CNN cognition conditions convolutional neural nets convolutional neural network deep features EEG signals electroencephalogram signals electroencephalography emotion recognition human emotions information source medical conditions medical diagnosis system medical signal processing Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications time‐order representation TOR transforms electroencephalography CNN brain-computer interfaces affective computing convolutional neural network time-order representation electroencephalogram signals transforms brain–computer interface deep features emotion recognition behaviour conditions medical signal processing cognition conditions human emotions medical conditions TOR information source medical diagnosis system EEG signals convolutional neural nets
ISSN:	0013-5194, 1350-911X, 1350-911X
Online Access:	Get full text
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