Enhancing robustness of spatial filters in motor imagery based brain-computer interface via temporal learning

Background

In motor imagery-based brain-computer interface (MI-BCI) EEG decoding, spatial filtering play a crucial role in feature extraction. Recent studies have emphasized the importance of temporal filtering for extracting discriminative features in MI tasks. While many efforts have been made to optimize feature extraction externally, stabilizing features from spatial filtering remains underexplored.

New method: To address this problem, we propose an approach to improve the robustness of temporal features by minimizing instability in the temporal domain. Specifically, we utilize Jensen-Shannon divergence to quantify temporal instability and integrate decision variables to construct an objective function that minimizes this instability. Our method enhances the stability of variance and mean values in the extracted features, improving the identification of discriminative features and reducing the effects of instability.

Results

The proposed method was applied to spatial filtering models, and tested on two publicly datasets as well as a self-collected dataset. Results demonstrate that the proposed method significantly boosts classification accuracy, confirming its effectiveness in enhancing temporal feature stability.

Comparison with existing methods: We compared our method with spatial filtering methods, and the-state-of-the-art models. The proposed approach achieves the highest accuracy, with 92.43 % on BCI competition III IVa dataset, 84.45 % on BCI competition IV 2a dataset, and 73.18 % on self-collected dataset.

Conclusions

Enhancing the instability of temporal features contributes to improved MI-BCI performance. This not only improves classification performance but also provides a stable foundation for future advancements. The proposed method shows great potential for EEG decoding.