Bhattacharyya Saugat, Hayashibe Mitsuhiro
School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Londonderry BT48 7JL, UK.
Department of Robotics, Tohoku University, Sendai 980-8579, Japan.
Brain Sci. 2021 Oct 23;11(11):1393. doi: 10.3390/brainsci11111393.
This study is aimed at the detection of single-trial feedback, perceived as erroneous by the user, using a transferable classification system while conducting a motor imagery brain-computer interfacing (BCI) task. The feedback received by the users are relayed from a functional electrical stimulation (FES) device and hence are somato-sensory in nature. The BCI system designed for this study activates an electrical stimulator placed on the left hand, right hand, left foot, and right foot of the user. Trials containing erroneous feedback can be detected from the neural signals in form of the error related potential (ErrP). The inclusion of neuro-feedback during the experiments indicated the possibility that ErrP signals can be evoked when the participant perceives an error from the feedback. Hence, to detect such feedback using ErrP, a transferable (offline) decoder based on optimal transport theory is introduced herein. The offline system detects single-trial erroneous trials from the feedback period of an online neuro-feedback BCI system. The results of the FES-based feedback BCI system were compared to a similar visual-based (VIS) feedback system. Using our framework, the error detector systems for both the FES and VIS feedback paradigms achieved an F1-score of 92.66% and 83.10%, respectively, and are significantly superior to a comparative system where an optimal transport was not used. It is expected that this form of transferable and automated error detection system compounded with a motor imagery system will augment the performance of a BCI and provide a better BCI-based neuro-rehabilitation protocol that has an error control mechanism embedded into it.
本研究旨在通过可转移分类系统，在进行运动想象脑机接口（BCI）任务时检测被用户视为错误的单次试验反馈。用户接收到的反馈是由功能性电刺激（FES）设备中继而来，因此本质上是体感的。本研究设计的BCI系统会激活放置在用户左手、右手、左脚和右脚上的电刺激器。包含错误反馈的试验可以从神经信号中以错误相关电位（ErrP）的形式检测出来。实验过程中加入神经反馈表明，当参与者从反馈中感知到错误时，有可能诱发ErrP信号。因此，为了使用ErrP检测此类反馈，本文引入了一种基于最优传输理论的可转移（离线）解码器。离线系统从在线神经反馈BCI系统的反馈期检测单次试验错误试验。将基于FES的反馈BCI系统的结果与类似的基于视觉（VIS）的反馈系统进行了比较。使用我们的框架，FES和VIS反馈范式的错误检测系统的F1分数分别达到了92.66%和83.10%，并且明显优于未使用最优传输的对比系统。预计这种可转移的自动错误检测系统与运动想象系统相结合，将提高BCI的性能，并提供一种更好的基于BCI的神经康复方案，其中嵌入了错误控制机制。
