Jeong, Ji Hyeok Kim, Keun-Tae Sung, Dong Jin Kim, Lae hyun Lee, Song Joo Kim, Dong-Joo Kim, Hyungmin 2026-02-26T10:00:05Z 2026-02-26T10:00:05Z 2025-12-01 2025-11-07 https://pubs.kist.re.kr/handle/201004/154382 Motor imagery (MI) brain computer interface (BCI) offer a non-invasive avenue for people with spinal cord injury (SCI) to volitionally control assistive technologies. However, MI-BCI performance in SCI is constrained by pronounced inter-subject variability, stemming from differences in injury level, chronicity, and the degree of cortical reorganization induced by sensory deafferentation rather than direct cortical damage. We propose a hierarchical subject-session transfer (HSST) method that first pre-trains on other subjects, then adapts with frozen features on target subject's support sessions, and finally personalizes on the target session using an CNN model backbone. In five SCI subjects (four sessions each), HSST achieved the highest accuracy (89.8±4.6%), surpassing ST (86.8±5.7%), CST (87.0±7.2%), and SST (87.8±7.5%). Korean 대한의용생체공학회 A Hierarchical Subject-Session Transfer Learning Strategy for Lower-Limb Motor Imagery Brain-Computer Interface in Individuals with Spinal Cord Injury 척수손상 장애인의 하지 운동 상상 기반 뇌-컴퓨터 인터페이스를 위한 계층적 사용자와 세션 간의 전이 학습 전략 Conference 2 대한의용생체공학회 2025 추계학술대회 , pp.279 - 280 대한의용생체공학회 2025 추계학술대회 279 280 KO 2025-11-06 대한의용생체공학회 2025 추계학술대회 초록집