  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.
However, the decoding accuracy of BCI drops severely across days due to the
neural data drift. While current recalibration techniques address this issue to
a degree, they often fail to leverage the limited labeled data, to consider the
signal correlation between two days, or to perform conditional alignment in
regression tasks. This paper introduces a novel approach to enhance
recalibration performance. We begin with preliminary experiments that reveal
the temporal patterns of neural signal changes and identify three critical
elements for effective recalibration: global alignment, conditional speed
alignment, and feature-label consistency. Building on these insights, we
propose the Speed-enhanced Subdomain Adaptation Regression (SSAR) framework,
integrating semi-supervised learning with domain adaptation techniques in
regression neural decoding. SSAR employs Speed-enhanced Subdomain Alignment
(SeSA) for global and speed conditional alignment of similarly labeled data,
with Contrastive Consistency Constraint (CCC) to enhance the alignment of SeSA
by reinforcing feature-label consistency through contrastive learning. Our
comprehensive set of experiments, both qualitative and quantitative,
substantiate the superior recalibration performance and robustness of SSAR.
