CNN-based Input-Aware Gradient Channel Selection for Motor Imagery Classification

Abstract

A CNN-based interpretable channel selection framework is proposed to reduce data complexity in session-transfer motor imagery (MI) brain–computer interface (BCI) scenarios. Experiments conducted on the BCI Competition IV-2a dataset with nine healthy subjects demonstrate that the proposed Input-Aware Gradient approach effectively preserves classification performance even when the number of channels is reduced to 10 (p = 0.11). These findings indicate that the proposed framework can identify a compact, subject-specific channel subset while retaining session-invariant neural features, thereby enabling the development of efficient and practical BCI systems.