Parallel Brain-Computer Interface: Collaborative Control and Perception Based on Multi-Brain Division of Labor

To date, brain-computer interfaces (BCIs) have adopted a serial working mechanism, causing performance bottlenecks due to time multiplication. In this study, we propose a parallel BCI (pBCI), which transforms the traditional sequential processing paradigm into multiple sub-paradigms based on division-labor mechanism, and then these sub-paradigms can be processed by multiple brains in a parallel and collaborative way. According to the proposed pBCI, we designed: 1) a Collaborative Speller paradigm where targets are encoded in a multi-dimensional space, with each brain focusing on a distinct dimension and collaborating on target localization; and 2) a Collaborative Perception paradigm with each brain contributing to a unique perspective of an autonomous vehicle and collaborating on perceiving dangers. We conducted both Collaborative P300 Speller experiments and Collaborative Perception experiments to validate the feasibility and practicality of the proposed pBCI. The results reveal that the proposed pBCI boosted BCI performance by multi-brain division-labor collaboration without increasing the workload for individuals, resulting in an information transfer rate improvement from 55.17bits/min to 146.35bits/min in Collaborative Speller, and a response time improvement from 1696ms to 1130ms in Collaborative Perception. In summary, the proposed pBCI provides a new and scalable BCI solution for more complex tasks and applications.