用于运动准备诱发脑电响应识别的新型复合空间滤波算法研究

Research on novel compound spatial filter algorithm for recognition of pre-movement EEG patterns

脑机接口(Brain-Computer Interface,BCI)是人类大脑与外界环境之间的直接信息交流通路,在人体运动功能康复、替代、增强等多方面日益凸显其重要科学意义与应用价值。基于运动意图的脑机接口范式作为最自然的脑机交互方式,受到研究者的广泛关注。对运动预备阶段的脑电特征进行解码能够使BCI响应速度更快、灵活度更高。然而,该阶段的脑电特征信号微弱,难以高效识别。针对上述挑战,围绕运动预备阶段脑电低频时域空间侧向性特征强化开展研究,设计了左右手自主按键运动任务,针对低频运动相关皮质电位特征,结合判别空间模式与任务相关成分分析两种算法,提出了一种新型复合空间模式滤波算法。实验结果显示,该算法平均识别正确率可达78.56%,优于已有文献报道,可为基于运动意图的高效脑机交互提供理论依据与技术支持。

Brain-computer interface(BCI)establishes a direct information communication channel between the brain and the external environment,which has increasingly highlighted its important scientific significance and application value in many aspects of human motor function rehabilitation,replacement and enhancement.As the most natural brain-computer interaction mode,BCI paradigm based on movement intention has been widely concerned by researchers.The decoding of pre-movement EEG patterns can make BCI more responsive and flexible.However,the EEG features at this stage are weak and difficult to recognize efficiently.Based on the above challenges,research is focused on the enhancement of temporal and spatial lateral characteristics of pre-movement EEG in low frequency.Aleft and right hand self-paced keystroke task is designed,and a new composite spatial filtering algorithm is proposed by combining discriminative canonical pattern matching(DCPM)and task-related component analysis(TRCA).The results show that the average accuracy of EEG signal recognition under this algorithm can reach 78.56%,which is better than previous literature reports,and can provide theoretical basis and technical support for efficient brain-computer interaction based on motion intention.