OBJECTIVE: In this study, the objective was to detect movement intentions and extract different levels of force and speed of the intended movement from scalp electroencephalography (EEG). We then estimated the performance of the closed loop system. APPROACH: Cued movements were detected from continuous EEG recordings using a template of the initial phase of the movement-related cortical potential in 12 healthy subjects. The temporal features, extracted from the movement intention, were classified with an optimized support vector machine. The system performance was evaluated when combining detection with classification. MAIN RESULTS: The system detected 81% of the movements and correctly classified 75 ± 9% and 80 ± 10% of these at the point of detection when varying the force and speed, respectively. When the detector was combined with the classifier, the system detected and correctly classified 64 ± 13% and 67 ± 13% of these movements. The system detected and incorrectly classified 21 ± 7% and 16 ± 9% of the movements. The movements were detected 317 ± 73 ms before the movement onset. SIGNIFICANCE: The results indicate that it is possible to detect movement intentions with limited latencies, and extract and classify different levels of force and speed, which may be combined with assistive technologies for patient-driven neurorehabilitation.
OBJECTIVE: In this study, the objective was to detect movement intentions and extract different levels of force and speed of the intended movement from scalp electroencephalography (EEG). We then estimated the performance of the closed loop system. APPROACH: Cued movements were detected from continuous EEG recordings using a template of the initial phase of the movement-related cortical potential in 12 healthy subjects. The temporal features, extracted from the movement intention, were classified with an optimized support vector machine. The system performance was evaluated when combining detection with classification. MAIN RESULTS: The system detected 81% of the movements and correctly classified 75 ± 9% and 80 ± 10% of these at the point of detection when varying the force and speed, respectively. When the detector was combined with the classifier, the system detected and correctly classified 64 ± 13% and 67 ± 13% of these movements. The system detected and incorrectly classified 21 ± 7% and 16 ± 9% of the movements. The movements were detected 317 ± 73 ms before the movement onset. SIGNIFICANCE: The results indicate that it is possible to detect movement intentions with limited latencies, and extract and classify different levels of force and speed, which may be combined with assistive technologies for patient-driven neurorehabilitation.
Authors: Nikunj A Bhagat; Anusha Venkatakrishnan; Berdakh Abibullaev; Edward J Artz; Nuray Yozbatiran; Amy A Blank; James French; Christof Karmonik; Robert G Grossman; Marcia K O'Malley; Gerard E Francisco; Jose L Contreras-Vidal Journal: Front Neurosci Date: 2016-03-31 Impact factor: 4.677