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作者:

Xu, Meng (Xu, Meng.) | Chen, Yuanfang (Chen, Yuanfang.) | Wang, Dan (Wang, Dan.) (学者:王丹) | Wang, Yijun (Wang, Yijun.) | Zhang, Lijian (Zhang, Lijian.) | Wei, Xiaoqian (Wei, Xiaoqian.)

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摘要:

Objective. Achieving high precision rapid serial visual presentation (RSVP) task often requires many electrode channels to obtain more information. However, the more channels may contain more redundant information and also lead to its limited practical applications. Therefore, it is necessary to reduce the number of channels to enhance the classification performance and users experience. Furthermore, cross-subject generalization has always been one of major challenges in electroencephalography channel reduction, especially in the RSVP paradigm. Most search-based channel selection method presented in the literature are single-objective methods, the classification accuracy (ACC) is usually chosen as the only criterion. Approach. In this article, the idea of multi-objective optimization was introduced into the RSVP channel selection to minimize two objectives: classification error and the number of channels. By combining a multi-objective evolutionary algorithm for solving large-scale sparse problems and hierarchical discriminant component analysis (HDCA), a novel channel selection method for RSVP was proposed. After that, the cross-subject generalization validation through the proposed channel selection method. Main results. The proposed method achieved an average ACC of 95.41% in a public dataset, which is 3.49% higher than HDCA. The ACC was increased by 2.73% and 2.52%, respectively. Besides, the cross-subject generalization models in channel selection, namely special-16 and special-32, on untrained subjects show that the classification performance is better than the Hoffmann empirical channels. Significance. The proposed channel selection method could reduce the calibration time in the experimental preparation phase and obtain a better accuracy, which is promising application in the RSVP scenario that requires low-density electrodes.

关键词:

multi-objective optimization electroencephalography (EEG) cross-subject generalization channel selection rapid serial visual presentation (RSVP)

作者机构:

  • [ 1 ] [Xu, Meng]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 2 ] [Wang, Dan]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 3 ] [Chen, Yuanfang]Beijing Inst Mech Equipment, Beijing, Peoples R China
  • [ 4 ] [Zhang, Lijian]Beijing Inst Mech Equipment, Beijing, Peoples R China
  • [ 5 ] [Wei, Xiaoqian]Beijing Inst Mech Equipment, Beijing, Peoples R China
  • [ 6 ] [Wang, Yijun]Chinese Acad Sci, Inst Semicond, State Key Lab Integrated Optoelect, Beijing, Peoples R China

通讯作者信息:

  • 王丹

    [Wang, Dan]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China

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来源 :

JOURNAL OF NEURAL ENGINEERING

ISSN: 1741-2560

年份: 2021

期: 4

卷: 18

4 . 0 0 0

JCR@2022

ESI学科: NEUROSCIENCE & BEHAVIOR;

ESI高被引阀值:71

JCR分区:2

被引次数:

WoS核心集被引频次: 11

SCOPUS被引频次: 14

ESI高被引论文在榜: 0 展开所有

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