A Convolutional Block Attention Module and Multi-band Fusion Network for Embedded AR-SSVEP BCI Systems

Brain-computer interfaces (BCI) based on augmented reality steady-state visual evoked potentials (AR-SSVEP) face critical challenges in mobile environments, including low signal-to-noise ratio (SNR) from dry electrodes and limited computational resources on mobile embedded platforms. To optimize the AR-SSVEP system performance, this study comprehensively considers the stimulus-response coupling mechanism integrating visual optical principles with deep learning-based classification. First, we designed an optimal AR visual stimulation configuration scheme capable of adaptively adjusting key parameters. Second, to address the time-varying non-stationary characteristics of SSVEP and inter-electrode quality variations in dry electrode systems, we propose CBAM-FNet-a lightweight SSVEP detection algorithm that incorporates the Convolutional Block Attention Module (CBAM) with multi-band fusion. The algorithm achieves classification accuracies of 93.84% on benchmark datasets and 74.43% on our self-collected AR-SSVEP dataset, representing performance improvements of up to 22.96% over state-of-the-art methods. Real-time implementation on an embedded unmanned vehicle platform demonstrates 65% control accuracy with an information transfer rate of 50.35 bits/min, validating the practical value of CBAM-FNet in embedded BCI applications and overcoming hardware-imposed performance limitations.