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.
Highlights
Functional restoration of arm and hand movements is a major goal of post-stroke rehabilitation therapy (Langhorne et al, 2009; Basteris et al, 2014)
Exoskeleton The MAHI Exo-II has four actuated degrees of freedom (DOF), but the current study only focused on controlling a single DOF elbow joint and the wrist and forearm actuators were held in a fixed position using set-point proportional-derivative control
Note that the negative peak of movement related cortical potentials (MRCPs) lags by ∼0.5 s with respect to movement-onset due to the non-linear phase distortion of IIR filters used for preprocessing EEG
Summary
Functional restoration of arm and hand movements is a major goal of post-stroke rehabilitation therapy (Langhorne et al, 2009; Basteris et al, 2014). There exists evidence to suggest that robotassisted therapy improves upper-limb functional assessment scores (Kwakkel et al, 2008; Klamroth-Marganska et al, 2014) and strength (Milot et al, 2013), by inducing activity-dependent cortical plasticity (Hogan et al, 2006; O’Malley et al, 2006; O’Dell et al, 2009) These improvements fail to reach relevant additional benefits over dose-matched conventional therapy (Kwakkel et al, 2008; Lo et al, 2010; Mehrholz et al, 2012; Klamroth-Marganska et al, 2014) or transfer into functional ability for performing daily living activities (Basteris et al, 2014). For more severely impaired patients and to ensure patient engagement, motor intent can be detected using noninvasive scalp electroencephalography (EEG; Wang et al, 2009; Gomez-Rodriguez et al, 2011; Frisoli et al, 2012; Venkatakrishnan et al, 2014), which is the focus of our work
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