Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors

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dc.citation.articleNumber122en_US
dc.citation.journalTitleFrontiers in Neuroscienceen_US
dc.citation.volumeNumber10en_US
dc.contributor.authorBhagat, Nikunj A.en_US
dc.contributor.authorVenkatakrishnan, Anushaen_US
dc.contributor.authorAbibullaev, Berdakhen_US
dc.contributor.authorArtz, Edward J.en_US
dc.contributor.authorYozbatiran, Nurayen_US
dc.contributor.authorBlank, Amy A.en_US
dc.contributor.authorFrench, Jamesen_US
dc.contributor.authorKarmonik, Christofen_US
dc.contributor.authorGrossman, Robert G.en_US
dc.contributor.authorO’Malley, Marcia K.en_US
dc.contributor.authorFrancisco, Gerard E.en_US
dc.contributor.authorContreras-Vidal, Jose L.en_US
dc.contributor.orgMechanical Engineeringen_US
dc.date.accessioned2016-06-27T18:44:08Zen_US
dc.date.available2016-06-27T18:44:08Zen_US
dc.date.issued2016en_US
dc.description.abstractThis 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.en_US
dc.identifier.citationBhagat, Nikunj A., Venkatakrishnan, Anusha, Abibullaev, Berdakh, et al.. "Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors." <i>Frontiers in Neuroscience,</i> 10, (2016) Frontiers Media S.A.: http://dx.doi.org/10.3389/fnins.2016.00122.en_US
dc.identifier.doihttp://dx.doi.org/10.3389/fnins.2016.00122en_US
dc.identifier.urihttps://hdl.handle.net/1911/90801en_US
dc.language.isoengen_US
dc.publisherFrontiers Media S.A.en_US
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleDesign and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivorsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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