Browsing by Author "Contreras-Vidal, Jose L."

Now showing 1 - 5 of 5
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    An exploration of grip force regulation with a low-impedance myoelectric prosthesis featuring referred haptic feedback
    (BioMed Central, 2015) Brown, Jeremy D.; Paek, Andrew; Syed, Mashaal; O'Malley, Marcia K.; Shewokis, Patricia A.; Contreras-Vidal, Jose L.; Davis, Alicia J.; Gillespie, R.B.
    Background: Haptic display technologies are well suited to relay proprioceptive, force, and contact cues from a prosthetic terminal device back to the residual limb and thereby reduce reliance on visual feedback. The ease with which an amputee interprets these haptic cues, however, likely depends on whether their dynamic signal behavior corresponds to expected behaviors—behaviors consonant with a natural limb coupled to the environment. A highly geared motor in a terminal device along with the associated high back-drive impedance influences dynamic interactions with the environment, creating effects not encountered with a natural limb. Here we explore grasp and lift performance with a backdrivable (low backdrive impedance) terminal device placed under proportional myoelectric position control that features referred haptic feedback. Methods: We fabricated a back-drivable terminal device that could be used by amputees and non-amputees alike and drove aperture (or grip force, when a stiff object was in its grasp) in proportion to a myoelectric signal drawn from a single muscle site in the forearm. In randomly ordered trials, we assessed the performance of N=10 participants (7 non-amputee, 3 amputee) attempting to grasp and lift an object using the terminal device under three feedback conditions (no feedback, vibrotactile feedback, and joint torque feedback), and two object weights that were indiscernible by vision. Results: Both non-amputee and amputee participants scaled their grip force according to the object weight. Our results showed only minor differences in grip force, grip/load force coordination, and slip as a function of sensory feedback condition, though the grip force at the point of lift-off for the heavier object was significantly greater for amputee participants in the presence of joint torque feedback. An examination of grip/load force phase plots revealed that our amputee participants used larger safety margins and demonstrated less coordination than our non-amputee participants. Conclusions: Our results suggest that a backdrivable terminal device may hold advantages over non-backdrivable devices by allowing grip/load force coordination consistent with behaviors observed in the natural limb. Likewise, the inconclusive effect of referred haptic feedback on grasp and lift performance suggests the need for additional testing that includes adequate training for participants.
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    Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors
    (Frontiers Media S.A., 2016) Bhagat, Nikunj A.; Venkatakrishnan, Anusha; Abibullaev, Berdakh; Artz, Edward J.; Yozbatiran, Nuray; Blank, Amy A.; French, James; Karmonik, Christof; Grossman, Robert G.; O’Malley, Marcia K.; Francisco, Gerard E.; Contreras-Vidal, Jose L.
    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.
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    Improving robotic stroke rehabilitation by incorporating neural intent detection: Preliminary results from a clinical trial
    (IEEE, 2017) Sullivan, Jennifer L.; Bhagat, Nikunj A.; Yozbatiran, Nuray; Paranjape, Ruta; Losey, Colin G.; Grossman, Robert G.; Contreras-Vidal, Jose L.; Francisco, Gerard E.; O’Malley, Marcia K.
    This paper presents the preliminary findings of a multi-year clinical study evaluating the effectiveness of adding a brain-machine interface (BMI) to the MAHI-Exo II, a robotic upper limb exoskeleton, for elbow flexion/extension rehabilitation in chronic stroke survivors. The BMI was used to trigger robot motion when movement intention was detected from subjects' neural signals, thus requiring that subjects be mentally engaged during robotic therapy. The first six subjects to complete the program have shown improvements in both Fugl-Meyer Upper-Extremity scores as well as in kinematic movement quality measures that relate to movement planning, coordination, and control. These results are encouraging and suggest that increasing subject engagement during therapy through the addition of an intent-detecting BMI enhances the effectiveness of standard robotic rehabilitation.
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    Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation
    (Elsevier, 2020) Bhagat, Nikunj A.; Yozbatiran, Nuray; Sullivan, Jennifer L.; Paranjape, Ruta; Losey, Colin; Hernandez, Zachary; Keser, Zafer; Grossman, Robert; Francisco, Gerard E.; O'Malley, Marcia K.; Contreras-Vidal, Jose L.; Mechatronics and Haptic Interfaces Laboratory
    Brain-machine interfaces (BMI) based on scalp EEG have the potential to promote cortical plasticity following stroke, which has been shown to improve motor recovery outcomes. However, the efficacy of BMI enabled robotic training for upper-limb recovery is seldom quantified using clinical, EEG-based, and kinematics-based metrics. Further, a movement related neural correlate that can predict the extent of motor recovery still remains elusive, which impedes the clinical translation of BMI-based stroke rehabilitation. To address above knowledge gaps, 10 chronic stroke individuals with stable baseline clinical scores were recruited to participate in 12 therapy sessions involving a BMI enabled powered exoskeleton for elbow training. On average, 132 ± 22 repetitions were performed per participant, per session. BMI accuracy across all sessions and subjects was 79 ± 18% with a false positives rate of 23 ± 20%. Post-training clinical assessments found that FMA for upper extremity and ARAT scores significantly improved over baseline by 3.92 ± 3.73 and 5.35 ± 4.62 points, respectively. Also, 80% participants (7 with moderate-mild impairment, 1 with severe impairment) achieved minimal clinically important difference (MCID: FMA-UE >5.2 or ARAT >5.7) during the course of the study. Kinematic measures indicate that, on average, participants’ movements became faster and smoother. Moreover, modulations in movement related cortical potentials, an EEG-based neural correlate measured contralateral to the impaired arm, were significantly correlated with ARAT scores (ρ = 0.72, p < 0.05) and marginally correlated with FMA-UE (ρ = 0.63, p = 0.051). This suggests higher activation of ipsi-lesional hemisphere post-intervention or inhibition of competing contra-lesional hemisphere, which may be evidence of neuroplasticity and cortical reorganization following BMI mediated rehabilitation therapy.
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    A Pre-Clinical Framework for Neural Control of a Therapeutic Upper-Limb Exoskeleton
    (IEEE, 2013) Blank, Amy; O'Malley, Marcia K.; Francisco, Gerard E.; Contreras-Vidal, Jose L.
    In this paper, we summarize a novel approach to robotic rehabilitation that capitalizes on the benefits of patient intent and real-time assessment of impairment. Specifically, an upper-limb, physical human-robot interface (the MAHI EXO-II robotic exoskeleton) is augmented with a noninvasive brain-machine interface (BMI) to include the patient in the control loop, thereby making the therapy “active” and engaging patients across a broad spectrum of impairment severity in the rehabilitation tasks. Robotic measures of motor impairment are derived from real-time sensor data from the MAHI EXO-II and the BMI. These measures can be validated through correlation with widely used clinical measures and used to drive patient-specific therapy sessions adapted to the capabilities of the individual, with the MAHI EXO-II providing assistance or challenging the participant as appropriate to maximize rehabilitation outcomes. This approach to robotic rehabilitation takes a step towards the seamless integration of BMIs and intelligent exoskeletons to create systems that can monitor and interface with brain activity and movement. Such systems will enable more focused study of various issues in development of devices and rehabilitation strategies, including interpretation of measurement data from a variety of sources, exploration of hypotheses regarding large scale brain function during robotic rehabilitation, and optimization of device design and training programs for restoring upper limb function after stroke.