Improved motor imagery classification using adaptive spatial filters based on particle swarm optimization algorithm

Xiong Xiong; Ying Wang; Tianyuan Song; Jinguo Huang; Guixia Kang

doi:10.3389/fnins.2023.1303648

Improved motor imagery classification using adaptive spatial filters based on particle swarm optimization algorithm

Front Neurosci. 2023 Dec 13:17:1303648. doi: 10.3389/fnins.2023.1303648. eCollection 2023.

Authors

Xiong Xiong^{1

2}, Ying Wang¹, Tianyuan Song¹, Jinguo Huang^{1

3}, Guixia Kang¹

Affiliations

¹ School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
² Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom.
³ School of Automation, Beijing University of Posts and Telecommunications, Beijing, China.

Abstract

Background: As a typical self-paced brain-computer interface (BCI) system, the motor imagery (MI) BCI has been widely applied in fields such as robot control, stroke rehabilitation, and assistance for patients with stroke or spinal cord injury. Many studies have focused on the traditional spatial filters obtained through the common spatial pattern (CSP) method. However, the CSP method can only obtain fixed spatial filters for specific input signals. In addition, the CSP method only focuses on the variance difference of two types of electroencephalogram (EEG) signals, so the decoding ability of EEG signals is limited.

Methods: To make up for these deficiencies, this study introduces a novel spatial filter-solving paradigm named adaptive spatial pattern (ASP), which aims to minimize the energy intra-class matrix and maximize the inter-class matrix of MI-EEG after spatial filtering. The filter bank adaptive and common spatial pattern (FBACSP), our proposed method for MI-EEG decoding, amalgamates ASP spatial filters with CSP features across multiple frequency bands. Through a dual-stage feature selection strategy, it employs the Particle Swarm Optimization algorithm for spatial filter optimization, surpassing traditional CSP approaches in MI classification. To streamline feature sets and enhance recognition efficiency, it first prunes CSP features in each frequency band using mutual information, followed by merging these with ASP features.

Results: Comparative experiments are conducted on two public datasets (2a and 2b) from BCI competition IV, which show the outstanding average recognition accuracy of FBACSP. The classification accuracy of the proposed method has reached 74.61 and 81.19% on datasets 2a and 2b, respectively. Compared with the baseline algorithm, filter bank common spatial pattern (FBCSP), the proposed algorithm improves by 11.44 and 7.11% on two datasets, respectively (p < 0.05).

Conclusion: It is demonstrated that FBACSP has a strong ability to decode MI-EEG. In addition, the analysis based on mutual information, t-SNE, and Shapley values further proves that ASP features have excellent decoding ability for MI-EEG signals and explains the improvement of classification performance by the introduction of ASP features. These findings may provide useful information to optimize EEG-based BCI systems and further improve the performance of non-invasive BCI.

Keywords: MI-EEG; brain computer Interface; common spatial pattern; particle swarm optimization; spatial filters.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors acknowledge financial support from the Fundamental Research Funds for the Central Universities (2020XD-A06-1), the State Key Program of the National Natural Science Foundation of China (82030037), the National Natural Science Foundation of China (62203063), and the BUPT Interdisciplinary Team Cultivation Program.