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.

基于粒子群优化算法的自适应空间滤波器解决方法 (ASP) 提出了一种训练和测试框架，用于运动意象脑机接口 (MI-BCI) 信号分类。在两个公共数据集上进行的比较实验显示了 FBCSP-ASP 方法的卓越平均识别准确率，该方法在 MI-BCI 上取得了显著的性能提升，对两个数据集的分类准确率分别达到了 74.61% 和 81.19%。与基线算法 (FBCSP) 相比，该算法在两个数据集上分别提高了 11.44% 和 7.11%。此外，基于互信息、t-SNE 和 Shapley 值的分析进一步证明了 ASP 特征对 MI-EEG 信号具有出色的解码能力，并解释了引入 ASP 特征后分类性能的改善。