期刊
ELECTRONICS LETTERS
卷 56, 期 25, 页码 1359-1361出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/el.2020.2380
关键词
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
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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