EDSF-Net : An enhanced dynamic spatiotemporal-frequency attention network for robust EEG decoding in motor imagery

Motor imagery is a non-invasive process that operates independently of external stimuli, and can be used to establish a direct connection between the brain and external devices solely through the imagination of a specific movement. Nonetheless, the complexity and variability of neural patterns pose substantial challenges, as accurately decoding motor imagery from electroencephalography signals remains a significant obstacle. This paper introduces an enhanced dynamic spatiotemporal -frequency attention convolutional neural network (EDSF-Net) for the precise decoding of motor imagery. EDSF-Net employs a refined spatiotemporal attention mechanism, grounded in enhanced dynamic convolution (EDConv), to emphasize localized spatial features alongside high and low-frequency temporal characteristics. Subsequently, EDConv is utilized for global spatial feature extraction. Following this, group convolutions formed by EDConv are implemented to fuse the extracted features effectively. Ultimately, a synchronized channel-frequency attention mechanism is employed to capture critical channel and frequency domain information, facilitating the model's focus on features most pertinent to the task throughout the learning process. We conducted a comprehensive evaluation of the performance of EDSF-Net on two public datasets, BCI Competition IV 2a and OpenBMI. In the hold-out session experiments, EDSF-Net achieved decoding accuracies of 84.26% and 75.14%, respectively. In the leave-one-subject-out experiments, EDSF-Net attained decoding accuracies of 66.78% and 82.24%, respectively. These results show that EDSF-Net has robust generalization capabilities, affirming its efficacy in addressing complex pattern recognition tasks, with significant potential for diverse applications.