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Multi-Task Heterogeneous Ensemble Learning-Based Cross-Subject EEG Classification Under Stroke Patients.
- Source :
-
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society [IEEE Trans Neural Syst Rehabil Eng] 2024; Vol. 32, pp. 1767-1778. Date of Electronic Publication: 2024 May 06. - Publication Year :
- 2024
-
Abstract
- 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 depending on the location of the stroke lesion, which should be considered. This paper introduces a multi-task electroencephalogram-based heterogeneous ensemble learning (MEEG-HEL) specifically designed for cross-subject training. In the proposed framework, common spatial patterns were used for feature extraction, and the features according to stroke lesions are shared and selected through sequential forward floating selection. The heterogeneous ensembles were used as classifiers. Nine patients with chronic ischemic stroke participated, engaging in MI and motor execution (ME) paradigms involving finger tapping. The classification criteria for the multi-task were established in two ways, taking into account the characteristics of stroke patients. In the cross-subject session, the first involved a direction recognition task for two-handed classification, achieving a performance of 0.7419 (±0.0811) in MI and 0.7061 (±0.1270) in ME. The second task focused on motor assessment for lesion location, resulting in a performance of 0.7457 (±0.1317) in MI and 0.6791 (±0.1253) in ME. Comparing the specific-subject session, except for ME on the motor assessment task, performance on both tasks was significantly higher than the cross-subject session. Furthermore, classification performance was similar to or statistically higher in cross-subject sessions compared to baseline models. The proposed MEEG-HEL holds promise in improving the practicality of neurorehabilitation in clinical settings and facilitating the detection of lesions.
- Subjects :
- Humans
Male
Female
Middle Aged
Aged
Imagination physiology
Stroke physiopathology
Stroke complications
Robotics
Adult
Psychomotor Performance
Ischemic Stroke physiopathology
Ischemic Stroke rehabilitation
Imagery, Psychotherapy methods
Electroencephalography methods
Stroke Rehabilitation methods
Brain-Computer Interfaces
Algorithms
Machine Learning
Subjects
Details
- Language :
- English
- ISSN :
- 1558-0210
- Volume :
- 32
- Database :
- MEDLINE
- Journal :
- IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
- Publication Type :
- Academic Journal
- Accession number :
- 38683717
- Full Text :
- https://doi.org/10.1109/TNSRE.2024.3395133