Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces

TL;DR

This paper introduces a parameter-free channel reflection data augmentation method that leverages prior knowledge of EEG channel distributions to improve classification accuracy in EEG-based brain-computer interfaces across various paradigms.

Contribution

The proposed channel reflection method is a novel, knowledge-driven data augmentation technique that enhances EEG decoding performance without additional parameters.

Findings

CR improves classification accuracy significantly.

CR outperforms existing data augmentation methods.

CR can be combined with other approaches for further gains.

Abstract

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.