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

SK Khare, A Nishad, A Upadhyay, V Bajaj - Electronics Letters, 2020 - Wiley Online Library
Electronics Letters, 2020Wiley Online Library
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 …
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.
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