Improving the Cross-Subject Performance of the ERP-Based Brain-Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank
Improving the Cross-Subject Performance of the ERP-Based Brain-Computer Interface Using Rapid Serial Visual Presentation and Correlation Analysis Rank
复制标题
使用快速串行视觉呈现和相关分析排名提高基于 ERP 的脑机接口的跨主题性能
DOI：
10.3389/fnhum.2020.00296
复制
发表时间：
2020-07-31
影响因子：
2.9
通讯作者：
Ming, Dong
中科院分区：
文献类型：
--
作者：
Liu, Shuang;Wang, Wei;Ming, Dong
脑机接口(BCI)是一种旨在通过计算机向任何人提供通信渠道的系统。最初，它被建议帮助残疾人，但实际上已经提出了更广泛的应用。然而，脑-机接口系统中的跨主体识别很难脱离个体的特性、不稳定的特性和环境的特性，这也给开发高可靠、高稳定的脑-机接口系统带来了困难。快速连续视觉呈现(RSVP)是最新的拼写方法之一，具有清晰、统一的背景和单一的刺激，可以诱发个体差异较小的事件相关电位(ERP)模式。为了建立一个新的BCI系统，允许新用户在不需要校准或校准时间较短的情况下直接使用它，采用RSVP作为诱发范式，提出了相关分析秩次(CAR)算法来改进跨个体分类，同时使用尽可能少的训练数据。58名受试者参加了实验。闪光刺激时间为200ms，关闭时间为100ms。P300组件按时间锁定到目标表示。结果表明，与矩阵范式相比，RSVP能在被试之间诱发出更多相似的ERP模式。然后计算并计算每个受试者之间的平均事件相关电位波形的夹角余弦。对于矩阵范式，所有被试的平均匹配数为6，而对于RSVP范式，当阈值设置为0.5时，平均匹配数范围为20，是阈值的3倍多，定量地表明，RSVP范式诱发的ERP波形产生的个体差异较小，更有利于跨被试分类。还计算了RSVP和矩阵范式的信息传递率(ITR)，RSVP范式的平均ITR为43.18比特/分钟，比矩阵范式高13%。然后，利用提出的CAR算法和传统的随机选择算法，计算并比较了接收机工作特性(ROC)曲线值。结果表明，该算法的性能明显优于传统的随机选择算法，其AUC值最高为0.8，而传统的随机算法仅为0.65。这些令人鼓舞的结果表明，在适当的诱发范式和分类方法下，基于ERP的脑-机接口在被试之间取得稳定的表现是可行的。因此，我们的研究结果为提高脑-机接口的性能提供了一种新的方法。
The brain-computer interface (BCI) is a system that is designed to provide communication channels to anyone through a computer. Initially, it was suggested to help the disabled, but actually had been proposed a wider range of applications. However, the cross-subject recognition in BCI systems is difficult to break apart from the individual specific characteristics, unsteady characteristics, and environmental specific characteristics, which also makes it difficult to develop highly reliable and highly stable BCI systems. Rapid serial visual presentation (RSVP) is one of the most recent spellers with a clean, unified background and a single stimulus, which may evoke event-related potential (ERP) patterns with less individual difference. In order to build a BCI system that allows new users to use it directly without calibration or with less calibration time, RSVP was employed as evoked paradigm, then correlation analysis rank (CAR) algorithm was proposed to improve the cross-individual classification and simultaneously use as less training data as possible. Fifty-eight subjects took part in the experiments. The flash stimulation time is 200 ms, and the off time is 100 ms. The P300 component was locked to the target representation by time. The results showed that RSVP could evoke more similar ERP patterns among subjects compared with matrix paradigm. Then, the included angle cosine was calculated and counted for averaged ERP waveform between each two subjects. The average matching number of all subjects was 6 for the matrix paradigm, while for the RSVP paradigm, the average matching number range was 20 when the threshold value was set to 0.5, more than three times as much larger, quantificationally indicating that ERP waveforms evoked by the RSVP paradigm produced smaller individual differences, and it is more favorable for cross-subject classification. Information transfer rates (ITR) were also calculated for RSVP and matrix paradigms, and the RSVP paradigm got the average ITR of 43.18 bits/min, which was 13% higher than the matrix paradigm. Then, the receiver operating characteristic (ROC) curve value was computed and compared using the proposed CAR algorithm and traditional random selection. The results showed that the proposed CAR got significantly better performance than the traditional random selection and got the best AUC value of 0.8, while the traditional random selection only achieved 0.65. These encouraging results suggest that with proper evoked paradigm and classification methods, it is feasible to get stable performance across subjects for ERP-based BCI. Thus, our findings provide a new approach to improve BCI performances.