Decoding a New Neural-Machine Interface for Control of Artificial Limbs

Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, IL, USA.
Journal of Neurophysiology (Impact Factor: 2.89). 12/2007; 98(5):2974-82. DOI: 10.1152/jn.00178.2007
Source: PubMed


An analysis of the motor control information content made available with a neural-machine interface (NMI) in four subjects is presented in this study. We have developed a novel NMI-called targeted muscle reinnervation (TMR)-to improve the function of artificial arms for amputees. TMR involves transferring the residual amputated nerves to nonfunctional muscles in amputees. The reinnervated muscles act as biological amplifiers of motor commands in the amputated nerves and the surface electromyogram (EMG) can be used to enhance control of a robotic arm. Although initial clinical success with TMR has been promising, the number of degrees of freedom of the robotic arm that can be controlled has been limited by the number of reinnervated muscle sites. In this study we assess how much control information can be extracted from reinnervated muscles using high-density surface EMG electrode arrays to record surface EMG signals over the reinnervated muscles. We then applied pattern classification techniques to the surface EMG signals. High accuracy was achieved in the classification of 16 intended arm, hand, and finger/thumb movements. Preliminary analyses of the required number of EMG channels and computational demands demonstrate clinical feasibility of these methods. This study indicates that TMR combined with pattern-recognition techniques has the potential to further improve the function of prosthetic limbs. In addition, the results demonstrate that the central motor control system is capable of eliciting complex efferent commands for a missing limb, in the absence of peripheral feedback and without retraining of the pathways involved.

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Available from: Levi J Hargrove,
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    • "Feature extraction is a necessary step for pattern recognition. As the effectiveness of time-domain (TD) feature set had been repeatedly demonstrated in previous studies on EMG pattern recognition [7] [24] [25], the TD features were adopted in this study. TD features were originally proposed by Hudgins et al. [7], where continuous EMG signals were segmented into multiple analysis windows and TD features were extracted from each analysis window. "
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    ABSTRACT: In this paper, a solution is proposed to predict the finger joint angle using electromyography (EMG) towards application for partial-hand amputees with functional wrist. In the experimental paradigm, the subject was instructed to continuously move one finger (middle finger for able-bodied subjects and index finger for partial-hand amputees) up to the maximum angle of flexion and extension while the wrist was conducting seven different wrist motions. A switching regime, including one linear discriminant analysis (LDA) classifier and fourteen state-space models, was proposed to continuously decode finger joint angles. LDA classifier was used to recognize which static wrist motion that the subject was conducting and choose the corresponding two state-space models for decoding joint angles of the finger with two degrees of freedom (DOFs). The average classification error rate (CER) was 6.18%, demonstrating that these seven static wrist motions along with the continuous movement of the finger could be classified. To improve the classification performance, a preprocessing method, class-wise stationary subspace analysis (cwSSA), was firstly adopted to extract the stationary components from original EMG data. Consequently, the average CER was reduced by 1.82% (p < 0.05). The state-space model was adopted to estimate the finger joint angle from EMG. The average estimation performance (index R2) of the two joint angles of the finger across seven static wrist motions achieved 0.843. This result shows that the finger's joint angles can be continuously estimated well while the wrist was conducting different static motions simultaneously. The average accuracy of seven static wrist motions with and without cwSSA and the average estimation performance of the two joint angles of the finger prove that the proposed switching regime is effective for continuous estimation of the finger joint angles under different static wrist motions from EMG.
    Biomedical Signal Processing and Control 11/2014; 14:265–271. DOI:10.1016/j.bspc.2014.08.004 · 1.42 Impact Factor
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    • "Studies involving targeted muscle reinnervation (Kuiken, Miller et al. 2005, Kuiken, Li et al. 2009, Bueno, French et al. 2011, Akhtar, Hargrove et al. 2012, Hebert and Lewicke 2012) suggest that simultaneous multi-dimensional control is possible. EMG pattern recognition control algorithms in robotic upper extremity prostheses routinely produce classification rates greater than 95% for multi-dimension joint movement (Khezri and Jahed 2007, Zhou, Lowery et al. 2007, Scheme, Hudgins et al. 2013, Wurth and Hargrove 2013). "
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    ABSTRACT: Objective: Powered robotic prostheses create a need for natural-feeling user interfaces and robust control schemes. Here, we examined the ability of a nonlinear autoregressive model to continuously map the kinematics of a transtibial prosthesis and electromyographic (EMG) activity recorded within socket to the future estimates of the prosthetic ankle angle in three transtibial amputees. Approach: Model performance was examined across subjects during level treadmill ambulation as a function of the size of the EMG sampling window and the temporal 'prediction' interval between the EMG/kinematic input and the model's estimate of future ankle angle to characterize the trade-off between model error, sampling window and prediction interval. Main result: Across subjects, deviations in the estimated ankle angle from the actual movement were robust to variations in the EMG sampling window and increased systematically with prediction interval. For prediction intervals up to 150 ms, the average error in the model estimate of ankle angle across the gait cycle was less than 6°. EMG contributions to the model prediction varied across subjects but were consistently localized to the transitions to/from single to double limb support and captured variations from the typical ankle kinematics during level walking. Significance: The use of an autoregressive modeling approach to continuously predict joint kinematics using natural residual muscle activity provides opportunities for direct (transparent) control of a prosthetic joint by the user. The model's predictive capability could prove particularly useful for overcoming delays in signal processing and actuation of the prosthesis, providing a more biomimetic ankle response.
    Journal of Neural Engineering 09/2014; 11(5):056027. DOI:10.1088/1741-2560/11/5/056027 · 3.30 Impact Factor
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    • "TMR is also suitable for other amputations levels. For example, analysis of high-density surface EMG signals shows that information corresponding to intrinsic hand-muscles may be decoded using pattern recognition (Zhou et al., 2007). Our clinical observation is that TMR amputees can control multiple hand grasps easier and more reliably than transradial amputees supporting the application of TMR to this population (Li et al., 2010). "
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    ABSTRACT: One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)-Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human-machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it.
    Frontiers in Neurorobotics 08/2014; 8:22. DOI:10.3389/fnbot.2014.00022
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