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Design and optimization of an EEG-based brain machine interface (BMI) to an upper-limb exoskeleton for stroke survivors

Authors :
Nikunj Arunkumar Bhagat
Anusha eVenkatakrishnan
Berdakh eAbibullaev
Edward J. Artz
Nuray eYozbatiran
Amy A. Blank
James eFrench
Christof eKarmonik
Robert G Grossman
Marcia K O'Malley
Gerard eFrancisco
Jose Luis Contreras-Vidal
Source :
Frontiers in Neuroscience, Vol 10 (2016)
Publication Year :
2016
Publisher :
Frontiers Media S.A., 2016.

Abstract

This study demonstrates the feasibility of detecting motor intent from brain activity of chronic stroke patients using an asynchronous electroencephalography (EEG)-based brain machine interface (BMI). Intent was inferred from movement related cortical potentials (MRCPs) measured over an optimized set of EEG electrodes. Successful intent detection triggered the motion of an upper-limb exoskeleton (MAHI Exo-II), to guide movement and to encourage active user participation by providing instantaneous sensory feedback. Several BMI design features were optimized to increase system performance in the presence of single-trial variability of MRCPs in the injured brain: 1) an adaptive time window was used for extracting features during BMI calibration; 2) training data from two consecutive days were pooled for BMI calibration to increase robustness to handle the day-to-day variations typical of EEG, and 3) BMI predictions were gated by residual electromyography (EMG) activity from the impaired arm, to reduce the number of false positives. This patient-specific BMI calibration approach can accommodate a broad spectrum of stroke patients with diverse motor capabilities. Following BMI optimization on day 3, testing of the closed-loop BMI-MAHI exoskeleton, on 4th and 5th days of the study, showed consistent BMI performance with overall mean true positive rate (TPR) = 62.7 +/- 21.4 % on day 4 and 67.1 +/- 14.6 % on day 5. The overall false positive rate (FPR) across subjects was 27.74 +/- 37.46 % on day 4 and 27.5 +/- 35.64 % on day 5; however for two subjects who had residual motor function and could benefit from the EMG-gated BMI, the mean FPR was quite low (< 10 %). On average, motor intent was detected -367 +/- 328 ms before movement onset during closed-loop operation. These findings provide evidence that closed-loop EEG-based BMI for stroke patients can be designed and optimized to perform well across multiple days without system recalibration.

Details

Language :
English
ISSN :
1662453X
Volume :
10
Database :
Directory of Open Access Journals
Journal :
Frontiers in Neuroscience
Publication Type :
Academic Journal
Accession number :
edsdoj.44d23ee739c8471bbe2a18574fcc2dd0
Document Type :
article
Full Text :
https://doi.org/10.3389/fnins.2016.00122