Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous systems that used fixed-length EEG trials for MI decoding, asynchronous BCIs aim to detect the user's MI without explicit triggers. They are challenging to implement, because the algorithm needs to first distinguish between resting-states and MI trials, and then classify the MI trials into the correct task, all without any triggers. This paper proposes a sliding window prescreening and classification (SWPC) approach for MI-based asynchronous BCIs, which consists of two modules: a prescreening module to screen MI trials out of the resting-state, and a classification module for MI classification. Both modules are trained with supervised learning followed by self-supervised learning, which refines the feature extractors. Within-subject and cross-subject asynchronous MI classifications on four different EEG datasets validated the effectiveness of SWPC, i.e., it always achieved the highest average classification accuracy, and outperformed the best state-of-the-art baseline on each dataset by about 2%.

翻译：基于运动想象的脑机接口通过想象不同身体部位的运动实现对外部设备的直接控制。与以往使用固定长度脑电试验进行运动想象解码的系统不同，异步脑机接口旨在无需显式触发的情况下检测用户的运动想象意图。其实现具有挑战性，因为算法需要首先区分静息状态与运动想象试验，然后在没有任何触发信号的情况下将运动想象试验正确分类至对应任务。本文提出一种用于基于运动想象的异步脑机接口的滑动窗口预筛选与分类方法，该方法包含两个模块：用于从静息状态中筛选运动想象试验的预筛选模块，以及用于运动想象分类的分类模块。两个模块均采用监督学习与自监督学习相结合的方式进行训练，以优化特征提取器。在四个不同脑电数据集上进行的被试内与跨被试异步运动想象分类实验验证了该方法的有效性，即其始终取得最高的平均分类准确率，并在每个数据集上以约2%的优势优于当前最佳基线方法。