Classification of motor imagery performance in acute stroke.
| Autor: | Tangwiriyasakul C; Neural Engineering, Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands. Clinical Neurophysiology, Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands., Mocioiu V, van Putten MJ, Rutten WL |
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| Jazyk: | angličtina |
| Zdroj: | Journal of neural engineering [J Neural Eng] 2014 Jun; Vol. 11 (3), pp. 036001. Date of Electronic Publication: 2014 Apr 16. |
| DOI: | 10.1088/1741-2560/11/3/036001 |
| Abstrakt: | Objective: Effective motor imagery performance, seen as strong suppression of the sensorimotor rhythm, is the key element in motor imagery therapy. Therefore, optimization of methods to classify whether the subject is performing the imagery task is a prerequisite. An optimal classification method should have high performance accuracy and use a small number of channels. We investigated the additional benefit of the common spatial pattern filtering (CSP) to a linear discriminant analysis (LDA) classifier, for different channel configurations. Methods: Ten hemispheric acute stroke patients and 11 healthy subjects were included. EEGs were recorded using 60 channels. The classifier was trained with a motor execution task. For both healthy controls and patients, analysis of recordings was initially limited to 3 and 11 electrodes recording from the motor cortex area, and later repeated using 45 electrodes. Results: No significant improvement on the addition of CSP to LDA was found (in both cases, the area under the receiving operating characteristic (AU-ROC) ≈ 0.70 (acceptable)). We then repeated the LDA+CSP method on recordings of 45 electrodes, since the use of imagery neuronal circuits may well extend beyond the motor area. AU-ROC rose to 0.90, but no virtual 'most responsible' electrode was observed. Finally, in mild-to-moderate stroke patients we could successfully use the EEG data recorded from the healthy hemisphere to train the classifier (AU-ROC ≈ 0.70). Significance: Including only the channels on the unaffected motor cortex is sufficient to train a classifier. |
| Databáze: | MEDLINE |
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