Virtual reality (VR)-based motor therapy is an emerging approach in neurorehabilitation. The combination of VR with electroencephalography (EEG) presents further opportunities to improve therapeutic efficacy by personalizing the paradigm. Specifically, the idea is to synchronize the choice and timing of stimuli in the perceived virtual world with fluctuating brain states relevant to motor behavior. Here, we present an open source EEG single-trial based classification pipeline that is designed to identify ongoing brain states predictive of the planning and execution of movements. 9 healthy volunteers each performed 1080 trials of a repetitive reaching task with an implicit two-alternative forced choice, i.e., use of the right or left hand, in response to the appearance of a visual target. The performance of the EEG decoding pipeline was assessed with respect to classification accuracy of right vs. left arm use, based on the EEG signal at the time of the stimulus. Different features, feature extraction methods, and classifiers were compared at different time windows; the number and location of informative EEG channels and the number of calibration trials needed were also quantified, as well as any benefits from individual-level optimization of pipeline parameters. This resulted in a set of recommended parameters that achieved an average 83.3% correct prediction on never-before-seen testing data, and a state-of-the-art 77.1% in a real-time simulation. Neurophysiological plausibility of the resulting classifiers was assessed by time–frequency and event-related potential analyses, as well as by Independent Component Analysis topographies and cortical source localization. We expect that this pipeline will facilitate the identification of relevant brain states as prospective therapeutic targets in closed-loop EEG-VR motor neurorehabilitation.

基于虚拟现实 (VR) 的运动疗法是神经康复中的一种新兴方法。VR 与脑电图 (EEG) 的结合为通过个性化范式提高治疗效果提供了更多机会。具体来说，这个想法是将感知虚拟世界中刺激的选择和时间与与运动行为相关的大脑状态波动同步。在这里，我们提出了一个基于开源 EEG 单次试验的分类管道，旨在识别预测运动计划和执行的持续大脑状态。9 名健康志愿者每人执行 1080 次重复伸手任务试验，其中包含隐含的两种选择强制选择，即使用右手或左手，以响应视觉目标的出现。根据刺激时的 EEG 信号，根据右臂与左臂使用的分类准确性评估 EEG 解码管道的性能。在不同的时间窗口比较不同的特征、特征提取方法和分类器；还量化了提供信息的 EEG 通道的数量和位置以及所需的校准试验的数量，以及管道参数的个体级优化带来的任何好处。这产生了一组推荐参数，在从未见过的测试数据上实现了平均 83.3% 的正确预测，并在实时模拟中达到了最先进的 77.1%。通过时间频率和事件相关电位分析评估所得分类器的神经生理学合理性，以及独立成分分析地形图和皮质源定位。我们预计这条管道将有助于识别相关的大脑状态，作为闭环 EEG-VR 运动神经康复中的预期治疗目标。