Volume 15, Issue 2 (Journal of Control, V.15, N.2 Summer 2021)                   JoC 2021, 15(2): 139-157 | Back to browse issues page

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Kalhor E, Bakhtiari B. Subject-Independent Channel and Feature Selection for Emotion Classification Based on EEG Signal: A Multi-Task Approach. JoC 2021; 15 (2) :139-157
URL: http://joc.kntu.ac.ir/article-1-695-en.html
1- , Sadjad University of Technology, Mashhad
Abstract:   (11635 Views)
Several researches have shown that emotion is a mental process and relates to the human’s brain. The emotion has impacts on important procedures, such as memory, concentration, thinking and decision-making. As a result, investigating the mechanism and performance of the emotion have attracted the cognitive science researchers’ attentions. In addition to clinical applications on quick detection, diagnosis and treatment of psychological disorders, investigating the emotion through biological signal processing can play an important role in human-computer communication-based sciences. This will result in progressive improvements in this field. Due to the fact that number of channels and features extracted out of the EEG signal are usually high, selecting relevant channels, with the aim of obtaining effective features, can have a prominent role in the efficiency of these methods. On the other hand, these features should result in the appropriate efficiency when encounter new subjects. In this paper, a multi-task approach is represented for emotion-related channel selection and proper subject-independent feature selection purposes. Moreover, to demonstrate the efficiency of the proposed method, DREAMER and DEAP datasets are used. Also, considering two emotional dimensions, including arousal and valance, some experiments are performed to show the desired efficiency of the proposed method for channel selection and subject-independent feature selection. Experimental results show that the proposed method has better efficiency in comparison with used methods.
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Type of Article: Research paper | Subject: Special
Received: 2019/07/22 | Accepted: 2020/06/27 | ePublished ahead of print: 2020/07/5

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