Speed-enhanced Subdomain Alignment for Long-term Stable Neural Decoding in Brain-computer Interfaces

Brain-computer interfaces (BCIs) offer a means to convert neural signals into control signals, providing a potential restoration of movement for people with paralysis. Despite their promise, BCIs face a significant challenge in maintaining decoding accuracy over time due to neural nonstationarities. While current recalibration techniques address this issue to a degree, they either fail to adequately exploit the limited labeled data, fail to perform conditional alignment in regression tasks, or overlook the signal correlation between data from two days. This paper proposes a novel Speed-enhanced Subdomain Alignment (SeSA) framework, integrating semi-supervised learning with domain adaptation techniques in regressive neural decoding. Specifically, SeSA carries out two alignments (i.e., global alignment and conditional speed alignment) to achieve recalibration. Our comprehensive set of experiments, both qualitative and quantitative, substantiate the superior recalibration performance and robustness of our proposed SeSA.