基于CMP的脑机接口并行算法的设计与实现

脑机接口作为一种新型的不依赖于人体外周神经系统及肌肉组织的人机交互手段受到了广泛的关注。然而由于脑电信号自身的复杂性以及复杂的模式识别算法,使得传统的单机处理模式无法满足脑机接口实时在线分析的要求。同时,处理器的发展已经进入到了多核时代,有着大量的计算资源可供使用。基于此,本文提出了脑机接口关键算法并行化的一般框架,并依据该框架对P300脑机接口的识别分类算法进行了并行化。理论分析和实验结果表明并行化能有效的提高脑机接口的通信速率,为脑电信号的在线分类识别提供了新的思路。

Design and Implementation of BCI Parallel Algorithm on CMP

Brain-computer interface as a new kind of human-computer interaction which does not depend on the human peripheral nervous system and muscle tissue has gotten wide range of concerns. However, due to the complexity of EEG, as well as the complex algorithms and high spatial and temporal resolution of EEG using for improving the recognition accuracy, the traditional serial algorithm can not meet brain-computer interface real-time online requirements. Meanwhile, multicore era is coming, and there are a lot of computing resources can be utilized. In this paper,we propose a general framework that paralleling the key algorithm of brain-computer interface; based on the framework we design and implement parallel algorithm on P300 brain-computer interface. Theoretical analysis and experimental results show that the parallelization can be effectively improve the communications efficiency of brain-computer interface, and provide a new way of thinking for online EEG classification.