Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 1767 - 1778
• Main Authors: Lee, Minji, Park, Hyeong-Yeong, Park, Wanjoo, Kim, Keun-Tae, Kim, Yun-Hee, Jeong, Ji-Hoon
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Aged; Algorithms; Brain-Computer Interfaces; Classification; cross-subject training; EEG; Electroencephalography; Electroencephalography - methods; Engines; Ensemble learning; Feature extraction; Female; Human-computer interface; Humans; Imagery, Psychotherapy - methods; Imagination - physiology; Interfaces; Ischemia; Ischemic Stroke - physiopathology; Ischemic Stroke - rehabilitation; Lesions; Machine Learning; Male; Mental task performance; Middle Aged; motor imagery; Motor task performance; Motors; multi-task heterogeneous ensemble learning; Multitasking; Neurology; Psychomotor Performance; Rehabilitation; Robotics; Statistical analysis; Stroke; Stroke (medical condition); Stroke - complications; Stroke - physiopathology; Stroke Rehabilitation - methods; Task analysis; Training

Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, the effectiveness of training with MI may vary dep...