Author(s): Xu, M (Xu, Meng); Chen, YF (Chen, Yuanfang); Wang, D (Wang, Dan); Wang, YJ (Wang, Yijun); Zhang, LJ (Zhang, Lijian); Wei, XQ (Wei, Xiaoqian)

Source: JOURNAL OF NEURAL ENGINEERING Volume: 18 Issue: 4 Article Number: 046076 DOI: 10.1088/1741-2552/ac0489 Published: AUG 2021

Abstract: 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.

Accession Number: WOS:000658478700001

PubMed ID: 34030144

Author Identifiers:

Author        Web of Science ResearcherID        ORCID Number

xu, meng                  0000-0002-3634-0547

ISSN: 1741-2560

eISSN: 1741-2552

Full Text: https://iopscience.iop.org/article/10.1088/1741-2552/ac0489