基于混合范式的脑-机接口异步控制方法研究

An Asynchronous Control Strategy Based on Hybrid BCI

自动判别使用者是否处于操作状态的异步控制问题,是脑-机接口(BCI)领域的研究热点之一。由于BCI范式主要针对区分思维指令来诱发脑电特征,导致在区分操作态上信息含量有限,难以获得较理想的异步识别结果。针对上述问题,研究一种结合事件相关电位(ERP)与稳态视觉诱发电位(SSVEP)的混合范式方法,以提升异步控制效果。采集10名被试3种状态下的19导联脑电信号,包括在正常使用BCI时的控制状态和被试目光不在BCI界面以及在睁眼静息状态下的2种空闲状态。通过提取不同状态下ERP的幅值特征与SSVEP的相关系数,区分不同的使用状态,进一步采用贝叶斯方法分别对ERP和SSVEP特征的控制态后验概率进行估计,然后将两者综合并采用阈值法对控制态和空闲态进行二分类识别。结果表明,所发展的混合范式方法在不同状态下两种特征差异同时存在,控制态下会产生更高的P300幅值与SSVEP相关系数,并且与空闲态下的特征之间存在统计学差异。对控制状态和空闲状态的总体平均识别正确率达到92.1%,AUC达到0.98。研究结果表明,混合范式方法在异步识别问题上有很好的潜力,值得进一步研究和发展。

Asynchronous control problems of automatically identifying whether users are controlling the system is a focal point of research in BCI.Traditional paradigms induce EEG characteristics by distinguishing different instructions,causing the limitation of the information,so it is difficult to obtain ideal results of asynchronous identification.Aiming to resolve the problem,we developed a hybrid BCI system containing event-related potential and steady-state visual evoked potential signals to promote the effect.In the study,we collected 19 channel EEG signals from 10 subjects under the control condition when the subjects used the BCI system normally and two idle conditions when subjects moved their eyes out of the screen and when they opened their eyes and rested.In order to identify the different conditions,we extracted the amplitude of the ERP and the correlation coefficient of SSVEP,then we estimated the posterior probability of the control state by ERP and SSVEP features,using a Bayesian method.Using 10 subjects’ EEG features,we found out that the hybrid paradigm performed better than a single paradigm,because the differences under the two paradigms coexisted.We achieved the accuracy of 92.1% and an AUC of 0.98 in identifying the control condition and the idle condition.This study demonstrated that hybrid had the ability to improve asynchronous identifying and it was worthy of a further study and development.