ECoG-Based Motor Imagery Classification Using Retentive Network

Abstract

Speech impairments from conditions like Amyotrophic Lateral Sclerosis and muscular dystrophy severely restrict patient communication, affecting daily and social life. Decoding technology based on Electrocorticography (ECoG) is essential for supporting these patients' communication. In this study, we propose a novel architecture combining a specialized convolutional layer for electrode feature extraction and a retentive network for ECoG signal classification of motor imagery, outperforming all baselines in accuracy.