A Concept for Extending the Applicability of Constraint-Induced Movement Therapy through Motor Cortex Activity Feedback Using a Neural Prosthesis

Department of Electronic Engineering, National University of Ireland, Maynooth, County Kildare, Ireland.
Computational Intelligence and Neuroscience (Impact Factor: 0.6). 02/2007; 2007:51363. DOI: 10.1155/2007/51363
Source: PubMed


This paper describes a concept for the extension of constraint-induced movement therapy (CIMT) through the use of feedback of primary motor cortex activity. CIMT requires residual movement to act as a source of feedback to the patient, thus preventing its application to those with no perceptible movement. It is proposed in this paper that it is possible to provide feedback of the motor cortex effort to the patient by measurement with near infrared spectroscopy (NIRS). Significant changes in such effort may be used to drive rehabilitative robotic actuators, for example. This may provide a possible avenue for extending CIMT to patients hitherto excluded as a result of severity of condition. In support of such a paradigm, this paper details the current status of CIMT and related attempts to extend rehabilitation therapy through the application of technology. An introduction to the relevant haemodynamics is given including a description of the basic technology behind a suitable NIRS system. An illustration of the proposed therapy is described using a simple NIRS system driving a robotic arm during simple upper-limb unilateral isometric contraction exercises with healthy subjects.

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    • "The work here is predicated on the attempted movement paradigm, which seeks to provide positive reinforcement feedback in response to successful engagement of the patient’s motor networks associated with the targeted motor task. It is speculated that such an approach can help reduce the possibility of the learned non-use phenomenon through the delivery of contingent rewards - a form of neurofeedback therapy [13]. "
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    ABSTRACT: Brain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor controlin a limb following stroke. BCIs are typically used by subjects with no damage to the brain thereforerelatively little is known about the technical requirements for the design of a rehabilitative BCI forstroke. 32-channel electroencephalogram (EEG) was recorded during a finger-tapping task from 10 healthysubjects for one session and 5 stroke patients for two sessions approximately 6 months apart. Anoff-line BCI design based on Filter Bank Common Spatial Patterns (FBCSP) was implemented to testand compare the efficacy and accuracy of training a rehabilitative BCI with both stroke-affected andhealthy data. Stroke-affected EEG datasets have lower 10-fold cross validation results than healthy EEG datasets.When training a BCI with healthy EEG, average classification accuracy of stroke-affected EEG islower than the average for healthy EEG. Classification accuracy of the late session stroke EEG isimproved by training the BCI on the corresponding early stroke EEG dataset. This exploratory study illustrates that stroke and the accompanying neuroplastic changes associatedwith the recovery process can cause significant inter-subject changes in the EEG features suitable formapping as part of a neurofeedback therapy, even when individuals have scored largely similar withconventional behavioural measures. It appears such measures can mask this individual variability incortical reorganization. Consequently we believe motor retraining BCI should initially be tailored toindividual patients.
    Full-text · Article · Jan 2014 · Journal of NeuroEngineering and Rehabilitation
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    • "There is evidence that neuroplasticity can be promoted using brain-computer interfaces (BCIs) [3]. Physical training, in which the patients are actively involved by means of a BCI, therefore has the potential to offer a novel form of active movement therapy to the severely impaired [4]. Our long-term goal is thus the development of a therapeutic intervention to restore hand function based on the automatic detection of movement intention directly from the brain, instead of relying on remaining motor function. "
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    ABSTRACT: Background Brain-computer interfaces (BCIs) were recently recognized as a method to promote neuroplastic effects in motor rehabilitation. The core of a BCI is a decoding stage by which signals from the brain are classified into different brain-states. The goal of this paper was to test the feasibility of a single trial classifier to detect motor execution based on signals from cortical motor regions, measured by functional near-infrared spectroscopy (fNIRS), and the response of the autonomic nervous system. An approach that allowed for individually tuned classifier topologies was opted for. This promises to be a first step towards a novel form of active movement therapy that could be operated and controlled by paretic patients. Methods Seven healthy subjects performed repetitions of an isometric finger pinching task, while changes in oxy- and deoxyhemoglobin concentrations were measured in the contralateral primary motor cortex and ventral premotor cortex using fNIRS. Simultaneously, heart rate, breathing rate, blood pressure and skin conductance response were measured. Hidden Markov models (HMM) were used to classify between active isometric pinching phases and rest. The classification performance (accuracy, sensitivity and specificity) was assessed for two types of input data: (i) fNIRS-signals only and (ii) fNIRS- and biosignals combined. Results fNIRS data were classified with an average accuracy of 79.4%, which increased significantly to 88.5% when biosignals were also included (p=0.02). Comparable increases were observed for the sensitivity (from 78.3% to 87.2%, p=0.008) and specificity (from 80.5% to 89.9%, p=0.062). Conclusions This study showed, for the first time, promising classification results with hemodynamic fNIRS data obtained from motor regions and simultaneously acquired biosignals. Combining fNIRS data with biosignals has a beneficial effect, opening new avenues for the development of brain-body-computer interfaces for rehabilitation applications. Further research is required to identify the contribution of each modality to the decoding capability of the subject’s hemodynamic and physiological state.
    Full-text · Article · Jan 2013 · Journal of NeuroEngineering and Rehabilitation
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    • "Recent studies on multiple subjects with different levels of experience in using a BCI for communication have shown that there are often between 5 and 30% of attempted BCI communications misclassified [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21]. Insufficient robustness and low accuracy and information transfer rate have prevented BCIs being offered to those who need it and thus there is an ongoing need to develop and optimize signal processing techniques for maximum performance and develop new techniques specifically suited to processing biological signals [22]. "
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    ABSTRACT: A recently proposed method for EEG preprocessing is extended and analyzed in this work via a range of different tests in combination with various other BCI components. Neural-time-series-prediction-processing (NTSPP) is a predictive approach to EEG preprocessing where prediction models (PMs) are trained to perform one-step-ahead prediction of the EEG times-series which reflect motor imagery induced alterations in neuronal activity. Due to the specialization of distinct PMs, the predicted signals (Ys) and error signals (Es) are distinctly different from the original (Os) signals. The PMs map the Os signals to a higher dimension which, in the majority of cases, produces features that are more separable than those produced by the Os signals. Four feature extraction procedures, ranging in complexity and in terms of the information which is extracted i.e., time domain, frequency domain and time–frequency (t–f) domain, are used to determine the separability enhancements which are verified by comparative statistical tests and brain–computer interface (BCI) tests on six subjects. It is shown that, in the majority of the tests, features extracted from the NTSPP signals are more separable than those extracted from the Os signals, in terms of increased Euclidean distance between class means, reduced inter-class correlations and intra-class variance, and higher classification accuracy (CA), information transfer (IT) rate and mutual information (MI).
    Full-text · Article · Jul 2010 · Biomedical Signal Processing and Control
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