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

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:
PublisherCopyrights
Funding Info:
Description:
(C) 2019 IEEE
ISSN:
1534-4320
1558-0210
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