[4N1-GS-1] Fundamental AI, theory: algorithm:

[4N1-GS-1-01] ECoG-Based Motor Imagery Classification Using Retentive Network

〇Shunya Nagashima¹, Kanta Kaneda¹, Tsumugi Iida¹, Misa Taguchi², Masayuki Hirata², Komei Sugiura¹ (1. Keio University, 2. Osaka University)

Keywords:Electrocorticography, BMI, Retentive Network

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.