AUTHOR=Xiong Xiong , Wang Ying , Song Tianyuan , Huang Jinguo , Kang Guixia 

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

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1303648

DOI=10.3389/fnins.2023.1303648

ISSN=1662-453X

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. 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.
Methods:
To make up for these deficiencies, this paper 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. 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 FBCSP, the proposed algorithm improves 11.44% and 7.11% on two datasets respectively (p<0.05). 
Conclusion:
It is demonstrated that FBACSP has a strong ability for decoding 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 noninvasive BCI.