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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Vydáno v:	Electronics letters Ročník 56; číslo 25; s. 1359 - 1361
Hlavní autoři:	Khare, S.K, Nishad, A, Upadhyay, A, Bajaj, V
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 10.12.2020
Témata:	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
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Popis
Shrnutí:	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 are one such source to capture and study human emotions. In this Letter, a novel time-order representation based on the S-transform and convolutional neural network (CNN) is proposed for the identification of human emotions. EEG signals are transformed into time-order representation (TOR) based on the S-transform. This TOR is given as an input to CNN to automatically extract and classify the deep features. Emotional states of happiness, fear, sadness, and relax are classified with an accuracy of 94.58%. The superiority of the method is judged by evaluating four performance parameters and comparing it with existing state-of-the-art on the same dataset.
ISSN:	0013-5194 1350-911X 1350-911X
DOI:	10.1049/el.2020.2380