Enhancing Spiking Rate Control in Brain-Computer Interfaces through Functional Connectivity-Based Grouping

Background: Brain-Computer Interfaces (BCIs) have transformed neuroscience and bioengineering by enabling direct communication between the brain and external devices. Effective neural signal decoding is crucial for BCI performance, yet it is often hindered by noise and instability in neuronal firing rates and patterns. New Method: This essay introduces an innovative approach to enhancing spiking rate control in BCIs through functional connectivity-based grouping. Functional connectivity refers to the statistical relationships between neural activities. The proposed methodology involves pre-recording analysis using techniques such as pairwise cross-correlation, Granger causality, and coherence to analyze functional connectivity. Neurons are then grouped into ''functional groups'' or ''microcircuits'' based on their connectivity profiles. This grouping is leveraged through adaptive filtering and dimensionality reduction to improve BCI performance. Results: The experimental results demonstrate that the functional connectivity-based grouping approach significantly enhances BCI performance. Benefits include noise reduction, increased stability, and improved neural signal decoding. Comparison with Existing Methods: Compared to existing methods, the proposed approach offers superior noise reduction and stability, leading to more accurate and reliable BCI performance. Conclusions: Our research highlights the potential of functional connectivity-based grouping in enhancing BCI performance. The findings underscore the importance of pilot testing, optimized decoding, and real-time adjustment, offering a promising avenue for future BCI development.