Prognostic and Monitory EEG-Biomarkers for BCI Upper-limb Stroke Rehabilitation

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Prognostic and Monitory EEG-Biomarkers for BCI Upper-limb Stroke Rehabilitation
Title:
Prognostic and Monitory EEG-Biomarkers for BCI Upper-limb Stroke Rehabilitation
Other Titles:
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publication Date:
24 June 2019
Citation:
R. Mane et al., "Prognostic and Monitory EEG-Biomarkers for BCI Upper-limb Stroke Rehabilitation," in IEEE Transactions on Neural Systems and Rehabilitation Engineering. doi: 10.1109/TNSRE.2019.2924742
Abstract:
With the availability of multiple rehabilitative interventions, identifying the one that elicits the best motor outcome based on the unique neuro-clinical profile of the stroke survivor is a challenging task. Predicting the potential of recovery using biomarkers specific to an intervention hence becomes important. To address this, we investigate intervention-specific prognostic and monitory biomarkers of motor function improvements using quantitative electroencephalography (QEEG) features in 19 chronic stroke patients following two different upper extremity rehabilitative interventions viz. Brain-Computer Interface (BCI) and transcranial Direct Current Stimulation coupled BCI (tDCSBCI). Brain symmetry index was found to be the best prognostic QEEG for clinical gains following BCI intervention (r = -0.80, p = 0.02), whereas power ratio index (PRI) was observed to be the best predictor for tDCS-BCI (r = -0.96, p = 0.004) intervention. Importantly, statistically significant between-intervention differences observed in the predictive capabilities of these features suggest that intervention-specific biomarkers can be identified. This approach can be further pursued to distinctly predict the expected response of a patient to available interventions. The intervention with the highest predicted gains may then be recommended to the patient, thereby enabling a personalised rehabilitation regime.
License type:
Publisher Copyright
Funding Info:
-
Description:
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1534-4320
1558-0210
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