A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-friendliness, usually a small amount of user-specific EEG data are used for calibration, which may not be enough to develop a pure data-driven decoding model. To cope with this typical calibration data shortage challenge in EEG-based BCIs, this paper proposes a parameter-free channel reflection (CR) data augmentation approach that incorporates prior knowledge on the channel distributions of different BCI paradigms in data augmentation. Experiments on eight public EEG datasets across four different BCI paradigms (motor imagery, steady-state visual evoked potential, P300, and seizure classifications) using different decoding algorithms demonstrated that: 1) CR is effective, i.e., it can noticeably improve the classification accuracy; 2) CR is robust, i.e., it consistently outperforms existing data augmentation approaches in the literature; and, 3) CR is flexible, i.e., it can be combined with other data augmentation approaches to further increase the performance. We suggest that data augmentation approaches like CR should be an essential step in EEG-based BCIs. Our code is available online.

翻译：脑机接口（BCI）实现了人脑与外部设备之间的直接通信。基于脑电图（EEG）的BCI是目前面向健全用户最流行的技术。为提高用户友好性，通常仅使用少量用户特定的EEG数据进行校准，这可能不足以构建纯数据驱动的解码模型。为应对基于EEG的BCI中这一典型的校准数据短缺挑战，本文提出一种无需参数的通道反射（CR）数据增强方法，该方法在数据增强过程中融入了不同BCI范式通道分布的先验知识。在涵盖四种不同BCI范式（运动想象、稳态视觉诱发电位、P300和癫痫发作分类）的八个公开EEG数据集上，使用不同解码算法进行的实验表明：1）CR具有有效性，即能显著提升分类准确率；2）CR具有鲁棒性，即其性能持续优于文献中现有的数据增强方法；3）CR具有灵活性，即可与其他数据增强方法结合以进一步提升性能。我们认为，类似CR的数据增强方法应成为基于EEG的BCI中不可或缺的环节。我们的代码已在线公开。