PlugSelect: Pruning Channels With Plug-and-Play Flexibility for Electroencephalography-Based Brain–Computer Interface

Automatic minimization and optimization of the number of electrodes are crucial for the practical application of electroencephalography (EEG)-based brain–computer interface (BCI). Previous methods typically require additional training costs or rely on prior knowledge assumptions. This study proposes a novel channel pruning model, PlugSelect, applicable across a broad range of BCI paradigms with no additional training costs and offering plug-and-play functionality. It integrates gradients along the input path to globally infer the causal relationships between input channels and outputs and ranks the contributions to identify the most highly attributed channels. The results showed that for three BCI paradigms, i.e., auditory attention decoding (AAD), motor imagery (MI), and affective computation (AC), PlugSelect could reduce the number of channels by at least half while effectively maintaining decoding performance and improving efficiency. The results benefit the design of wearable EEG-based devices, facilitating the practical application of BCI technology.