Online Adaptive Decoding for MI-BCI Based on Stimulation and Feature Optimization and Data Augmentation

Motor imagery-based brain-computer interfaces (MI-BCIs) have been extensively researched. However, how to accurately recognize lower limb motion intentions, especially those of the left and right feet/legs, has not been well addressed. In this study, an efficient MI-BCI online decoding method, based on the deliberately designed functional electrical stimulation (FES) guidance and algorithms for feature extraction and model adaptation, was proposed. First, a method for designing the FES current curve based on muscle activation was proposed, by which an enhanced MI-BCI for gait training was designed and applied to improve the subjects' motor imagery abilities and the separability of the associated electroencephalogram (EEG) signals. Then, a random filter bank-based common spatial pattern (CSP) algorithm was developed for feature extraction, by which the subject-specific optimal filter bank can be obtained and the EEG separability can be further improved. Moreover, an online adaptation algorithm based on data augmentation and model retraining was proposed to rapidly regulate the decoder to suit the subject's status. Finally, extensive experiments were carried out, and it was shown by the results that the performance of online decoding can be significantly raised by the proposed methods.