ViF-SD2E: A Robust Weakly-Supervised Method for Neural Decoding
This addresses the challenge of high labeling costs and noise in neural decoding for brain-computer interfaces, though it appears incremental as it builds on existing observations and methods.
The authors tackled the problem of neural decoding for finger movement tracking from macaque neural signals by proposing ViF-SD2E, a robust weakly-supervised method that exploits spatial and temporal information, achieving performance sometimes comparable to supervised methods.
Neural decoding plays a vital role in the interaction between the brain and the outside world. In this paper, we directly decode the movement track of a finger based on the neural signals of a macaque. Supervised regression methods may overfit to actual labels containing noise, and require a high labeling cost, while unsupervised approaches often have unsatisfactory accuracy. Besides, the spatial and temporal information is often ignored or not well exploited by those methods. This motivates us to propose a robust weakly-supervised method, called ViF-SD2E, for neural decoding. In particular, it consists of a space-division (SD) module and a exploration--exploitation (2E) strategy, to effectively exploit both the spatial information of the outside world and the temporal information of neural activity, where the SD2E output is analogized with the weak 0/1 vision-feedback (ViF) label for training. It is worth noting that the designed ViF-SD2E is based on a symmetric phenomenon between the unsupervised decoding trajectory and the real trajectory in previous observations, then a cognitive pattern of fuzzy (robust) interaction in the nervous system may be discovered by us. Extensive experiments demonstrate the effectiveness of our method, which can be sometimes comparable to supervised counterparts.