Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: An offline study in patients with tetraplegia
Number of pages
SourceIEEE Transactions on Neural Systems and Rehabilitation Engineering, 22, 2, (2014), pp. 222-229
Article / Letter to editor
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SW OZ DCC AI
IEEE Transactions on Neural Systems and Rehabilitation Engineering
SubjectCognitive artificial intelligence; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication; Radboudumc 16: Vascular damage RIHS: Radboud Institute for Health Sciences; Radboudumc 18: Healthcare improvement science DCMN: Donders Center for Medical Neuroscience; Radboudumc 18: Healthcare improvement science RIHS: Radboud Institute for Health Sciences; Radboudumc 6: Metabolic Disorders RIHS: Radboud Institute for Health Sciences
Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.
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