FasterEEG: Adaptive Channel and Model Size Optimization for Efficient Brain Decoding

Existing methods for electroencephalography (EEG)-based brain decoding mainly emphasize decoding accuracy, whereas decoding efficiency has been rarely considered, restricting their practical deployment in resource-constrained applications. Crucial channel selection-based data compression can improve decoding efficiency, but the number and distribution of selected channels are typically fixed across trials, which limits flexibility. Similarly, previous studies usually employ fixed-size models for all samples, leading to redundant computations, especially for simple trials. In this paper, in contrast to the commonly used one-size-fits-all scheme, we propose FasterEEG, a novel and differentiable framework that adaptively adjusts both the input channel number and model size according to each sample. First, an event-related desynchronization/synchronization (ERD/ERS)-based channel transformation strategy is designed to compress full-channel EEG signals into fewer channels. Then, to improve both decoding accuracy and efficiency, a lightweight policy network is introduced to determine the optimal number of input channels and decoding model size for final classification. Moreover, to overcome the non-differentiability of channel and model size selection, a Gumbel-Estimator-based collaborative optimization method is developed to jointly train the policy and classification networks. Extensive experiments on multiple baseline models and datasets were conducted to validate the superiority of FasterEEG in brain decoding acceleration. Results showed that FasterEEG consistently improved decoding efficiency while maintaining classification accuracy. In particular, compared with the lightweight ShallowConvNet, our method achieved 1.89% higher decoding accuracy while requiring only 28% of the computational cost, thereby demonstrating the feasibility and generalization ability of the proposed framework for efficient brain decoding.