Classification of emotions from EEG signals using time‐order representation based on the S‐transform and convolutional neural network. Issue 25 (14th October 2020)

• Record Type: Journal Article
• Authors: Khare, S.K.; Nishad, A.; Upadhyay, A.; Bajaj, V.
• Is Part Of: Electronics letters. Volume 56:Issue 25(2020)
• Page Start: 1359
• Page End: 1361
• Publication Date: 2020-10-14
• Subjects: medical signal processing -- emotion recognition -- electroencephalography -- convolutional neural nets -- transforms -- brain‐computer interfaces; medical conditions -- affective computing -- brain–computer interface -- medical diagnosis system -- electroencephalogram signals -- human emotions -- convolutional neural network -- CNN -- EEG signals -- TOR -- information source -- time‐order representation -- behaviour conditions -- cognition conditions -- deep features; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2020.2380
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : 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.