Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces
This addresses the problem of limited user-specific data for able-bodied users in EEG-based BCIs, offering an incremental improvement over existing data augmentation methods.
The paper tackles the calibration data shortage challenge in EEG-based brain-computer interfaces by proposing a parameter-free channel reflection data augmentation approach that incorporates prior knowledge on channel distributions, demonstrating effectiveness, robustness, and flexibility across eight public EEG datasets and four BCI paradigms with improved classification accuracy.
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.