Open AccessClassification of emotions from EEG signals using time‐order representation based on the S‐transform and convolutional neural network
Author(s) -
Khare S.K.,
Nishad A.,
Upadhyay A.,
Bajaj V.
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2020.2380
Subject(s) - convolutional neural network , sadness , computer science , electroencephalography , artificial intelligence , representation (politics) , pattern recognition (psychology) , emotion classification , brain–computer interface , speech recognition , happiness , anger , psychology , law , social psychology , psychiatry , politics , political science
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.