A portable HCI system‐oriented EEG feature extraction and channel selection for emotion recognition. Issue 1 (8th October 2020)

• Record Type: Journal Article
• Authors: Zheng, Xiangwei; Liu, Xiaofeng; Zhang, Yuang; Cui, Lizhen; Yu, Xiaomei
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 1(2021)
• Page Start: 152
• Page End: 176
• Publication Date: 2020-10-08
• Subjects: channel selection -- emotion recognition -- feature extraction -- human–computer interaction -- relief; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22295
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Emotion recognition has become an important component of human–computer interaction systems. Research on emotion recognition based on electroencephalogram (EEG) signals are mostly conducted by the analysis of all channels' EEG signals. Although some progresses are achieved, there are still several challenges such as high dimensions, correlation between different features and feature redundancy in the realistic experimental process. These challenges have hindered the applications of emotion recognition to portable human–computer interaction systems (or devices). This paper explores how to find out the most effective EEG features and channels for emotion recognition so as to only collect data as less as possible. First, discriminative features of EEG signals from different dimensionalities are extracted for emotion classification, including the first difference, multiscale permutation entropy, Higuchi fractal dimension, and discrete wavelet transform. Second, relief algorithm and floating generalized sequential backward selection algorithm are integrated as a novel channel selection method. Then, support vector machine is employed to classify the emotions for verifying the performance of the channel selection method and extracted features. At last, experimental results demonstrate that the optimal channel set, which are mostly located at the frontal, has extremely high similarity on the self‐collected data set and the public data set and the average classification accuracy is achieved up to 91.31% with the selected 10‐channel EEG signals. The findings are valuable for the practical EEG‐based emotion recognition systems.