Heterogeneous domain adaptation for intracortical signal classification using domain consensus

Most domain adaptation studies have made efforts to solve the problem of homogeneous data shifts, which share the same feature and label space in both domains. However, for the heterogeneous scenarios, it still needs improvements in practical applications for intracortical brain-computer interface (iBCI) systems. To address this issue, we propose a domain adaptation framework for decoding reaching and grasping movements of iBCI systems in two heterogeneous scenarios, i.e., partial domain adaptation (PDA) and open set domain adaptation (OSDA). The proposed framework combined the neural activity vector (NAV) features and the domain consensus clustering (DCC) method for iBCI classification. For heterogeneous scenarios, the framework can map common classes according to the clusters with the highest domain consensus score at the semantic-level and distinguish private classes at the sample-level. We evaluated the framework on the data, which were collected from three array electrodes implanted in three regions of cerebral cortexes from one monkey across two sessions, each consisting of four continuous days. The proposed framework outperformed than four PDA and two OSDA methods, which effectively reduced the disparity of feature and label space in two heterogeneous scenarios. Consequently, our results demonstrated that the proposed framework is a new solution for decoder calibration in two heterogeneous scenarios, and it facilitates the generality of the iBCI system for clinical applications.