Game Theory-Based Adaptive Human-Machine Joint Learning for Online MI-BCI Decoding.

Motor imagery based brain-computer interface (MI-BCI) has been extensively researched for neurorehabilitation and motor assistance, while the performance of previous MI-BCI systems for online decoding motor intentions is still not satisfactory. Therefore, a game theory-based adaptive human-machine joint (AHMJ) learning method integrating subject learning and decoder updating was proposed for MI-BCI decoding, by which multi-class MI for unilateral upper limb can be successfully decoded online. On the one hand, a novel MI training method was proposed to facilitate subjects' learning to generate separable electroencephalogram (EEG) data, where the MI training process was modeled as a two-player zero-sum minimax game and the task difficulty was adaptively regulated according to each subject's performance by solving the minimax problem. On the other hand, a new online adaptive algorithm was designed to ensure stable updating of the decoding model, integrating knowledge distillation and prototype-guided domain adaptation for different MI training intervals. Online MI-BCI experiment on a total of fourteen healthy subjects and online simulation experiment on a public dataset from twenty-five healthy subjects were conducted. Compared with the traditional method that relies solely on subject learning and the previous human-machine joint learning method, the average decoding accuracy was significantly improved by 9.7% and 5.6% (paired t-test, both $p < 0.01$), respectively. The online adaptive algorithm also outperformed previous updating approaches in both accuracy and stability. The proposed AHMJ learning method can be applied to improve the online MI-BCI decoding accuracy for neurorehabilitation and motor assistance.