Improved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm

• URL: http://arxiv.org/abs/2310.19202v1
• Date: Sun, 29 Oct 2023 23:53:37 GMT
• Title: Improved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm
• Authors: Xiong Xiong, Ying Wang, Tianyuan Song, Jinguo Huang, Guixia Kang
• Abstract summary: This paper proposes an adaptive spatial filter solving method based on particle swarm optimization algorithm (PSO) A training and testing framework based on filter bank and spatial filters (FBCSP-ASP) is designed for MI EEG signal classification. The classification accuracy of the proposed method has reached 74.61% and 81.19% on datasets 2a and 2b, respectively.
• Score: 4.93693103484175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Besides, CSP method only focuses on the variance difference of two types of electroencephalogram (EEG) signals, so the decoding ability of EEG signals is limited. To obtain more effective spatial filters for better extraction of spatial features that can improve classification to MI-EEG, this paper proposes an adaptive spatial filter solving method based on particle swarm optimization algorithm (PSO). A training and testing framework based on filter bank and spatial filters (FBCSP-ASP) is designed for MI EEG signal classification. Comparative experiments are conducted on two public datasets (2a and 2b) from BCI competition IV, which show the outstanding average recognition accuracy of FBCSP-ASP. The proposed method has achieved significant performance improvement on MI-BCI. 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 (FBCSP), the proposed algorithm improves 11.44% and 7.11% on two datasets respectively. Furthermore, 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.

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