利用脑电通道先验知识增强数据，提升小样本脑机接口性能
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces
- 通过反射通道分布实现无参数数据增强
- 在8个数据集上显著提升分类准确率
- 兼容其他增强方法，适合资源有限的BCI应用
脑机接口（BCI）实现大脑与外部设备的直接通信。基于脑电图（EEG）的BCI目前是健全用户最常用的方式。为提高易用性，通常仅使用少量用户特定的EEG数据进行校准，这可能不足以训练纯数据驱动的解码模型。针对这一典型的小样本挑战，本文提出一种无需参数的通道反射（CR）数据增强方法，将不同BCI范式中通道分布的先验知识融入数据增强过程。在四个不同BCI范式（运动想象、稳态视觉诱发电位、P300、癫痫分类）的八个公开EEG数据集上，采用不同解码算法进行实验表明：1）CR有效，可显著提升分类准确率；2）CR鲁棒，始终优于现有数据增强方法；3）CR灵活，可与其他增强方法结合进一步提升性能。我们建议类似CR的数据增强应成为基于EEG的BCI中的必要步骤。代码已公开。
原文摘要 · Abstract (English)
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.
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