An exploration of EEG features during recovery following stroke - implications for BCI-mediated neurorehabilitation therapy

Hdl Handle:
http://hdl.handle.net/10147/316090
Title:
An exploration of EEG features during recovery following stroke - implications for BCI-mediated neurorehabilitation therapy
Authors:
Leamy, Darren J; Kocijan, Juš; Domijan, Katarina; Duffin, Joseph; Roche, Richard AP; Commins, Sean; Collins R; Ward, Tomas E
Citation:
Journal of NeuroEngineering and Rehabilitation. 2014 Jan 28;11(1):9
Issue Date:
28-Jan-2014
URI:
http://dx.doi.org/10.1186/1743-0003-11-9; http://hdl.handle.net/10147/316090
Abstract:
Abstract Background Brain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor control in a limb following stroke. BCIs are typically used by subjects with no damage to the brain therefore relatively little is known about the technical requirements for the design of a rehabilitative BCI for stroke. Methods 32-channel electroencephalogram (EEG) was recorded during a finger-tapping task from 10 healthy subjects for one session and 5 stroke patients for two sessions approximately 6 months apart. An off-line BCI design based on Filter Bank Common Spatial Patterns (FBCSP) was implemented to test and compare the efficacy and accuracy of training a rehabilitative BCI with both stroke-affected and healthy data. Results Stroke-affected EEG datasets have lower 10-fold cross validation results than healthy EEG datasets. When training a BCI with healthy EEG, average classification accuracy of stroke-affected EEG is lower than the average for healthy EEG. Classification accuracy of the late session stroke EEG is improved by training the BCI on the corresponding early stroke EEG dataset. Conclusions This exploratory study illustrates that stroke and the accompanying neuroplastic changes associated with the recovery process can cause significant inter-subject changes in the EEG features suitable for mapping as part of a neurofeedback therapy, even when individuals have scored largely similar with conventional behavioural measures. It appears such measures can mask this individual variability in cortical reorganization. Consequently we believe motor retraining BCI should initially be tailored to individual patients.
Language:
en
Keywords:
STROKE AND TIA

Full metadata record

DC FieldValue Language
dc.contributor.authorLeamy, Darren Jen_GB
dc.contributor.authorKocijan, Jušen_GB
dc.contributor.authorDomijan, Katarinaen_GB
dc.contributor.authorDuffin, Josephen_GB
dc.contributor.authorRoche, Richard APen_GB
dc.contributor.authorCommins, Seanen_GB
dc.contributor.authorCollins Ren_GB
dc.contributor.authorWard, Tomas Een_GB
dc.date.accessioned2014-04-24T11:27:19Z-
dc.date.available2014-04-24T11:27:19Z-
dc.date.issued2014-01-28-
dc.identifier.citationJournal of NeuroEngineering and Rehabilitation. 2014 Jan 28;11(1):9en_GB
dc.identifier.urihttp://dx.doi.org/10.1186/1743-0003-11-9-
dc.identifier.urihttp://hdl.handle.net/10147/316090-
dc.description.abstractAbstract Background Brain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor control in a limb following stroke. BCIs are typically used by subjects with no damage to the brain therefore relatively little is known about the technical requirements for the design of a rehabilitative BCI for stroke. Methods 32-channel electroencephalogram (EEG) was recorded during a finger-tapping task from 10 healthy subjects for one session and 5 stroke patients for two sessions approximately 6 months apart. An off-line BCI design based on Filter Bank Common Spatial Patterns (FBCSP) was implemented to test and compare the efficacy and accuracy of training a rehabilitative BCI with both stroke-affected and healthy data. Results Stroke-affected EEG datasets have lower 10-fold cross validation results than healthy EEG datasets. When training a BCI with healthy EEG, average classification accuracy of stroke-affected EEG is lower than the average for healthy EEG. Classification accuracy of the late session stroke EEG is improved by training the BCI on the corresponding early stroke EEG dataset. Conclusions This exploratory study illustrates that stroke and the accompanying neuroplastic changes associated with the recovery process can cause significant inter-subject changes in the EEG features suitable for mapping as part of a neurofeedback therapy, even when individuals have scored largely similar with conventional behavioural measures. It appears such measures can mask this individual variability in cortical reorganization. Consequently we believe motor retraining BCI should initially be tailored to individual patients.-
dc.language.isoenen
dc.subjectSTROKE AND TIAen_GB
dc.titleAn exploration of EEG features during recovery following stroke - implications for BCI-mediated neurorehabilitation therapyen_GB
dc.language.rfc3066en-
dc.rights.holderDarren J Leamy et al.; licensee BioMed Central Ltd.-
dc.description.statusPeer Reviewed-
dc.date.updated2014-04-21T07:48:58Z-
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