The Evoked Potential Operant Conditioning System (EPOCS): A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders

The Evoked Potential Operant Conditioning System (EPOCS) is a software tool that implements protocols for operantly conditioning stimulus-triggered muscle ...

Stratton VA Medical Center

State University of New York at Albany

In this paper, we report the performance of 9-11-year-old children using a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) and provide control data collected from adults for comparison. Children in our study achieved a much higher performance (79% accuracy; average age 9.64 years old) than the only previous investigation of children using an SSVEP-based BCI (∼50% accuracy; average age 9.86 years old).

The performance of 9-11-year-old children using an SSVEP-based BCI for target selection.

Neurological disorders, such as spinal cord injury, stroke, traumatic brain injury, cerebral palsy, and multiple sclerosis cause motor impairments that are a huge burden at the individual, family, and societal levels. Spinal reflex abnormalities contribute to these impairments. Spinal reflex measurements play important roles in characterizing and monitoring neurological disorders and their associated motor impairments, such as spasticity, which affects nearly half of those with neurological disorders. Spinal reflexes can also serve as therapeutic targets themselves. Operant conditioning protocols can target beneficial plasticity to key reflex pathways; they can thereby trigger wider plasticity that improves impaired motor skills, such as locomotion. These protocols may complement standard therapies such as locomotor training and enhance functional recovery. This paper reviews the value of spinal reflexes and the therapeutic promise of spinal reflex operant conditioning protocols; it also considers the complex process of translating this promise into clinical reality.

Retraining Reflexes: Clinical Translation of Spinal Reflex Operant Conditioning.

Operant conditioning of the spinal stretch reflex (SSR) or its electrical analog, the H-reflex, is a valuable experimental paradigm for studying the acquisition and maintenance of a simple motor skill. The CNS substrate of this skill consists of brain and spinal cord plasticity that operates as a hierarchy-the learning experience induces plasticity in the brain that guides and maintains plasticity in the spinal cord. This is apparent in the two components of the skill acquisition: task-dependent adaptation, reflecting brain plasticity; and long-term change, reflecting gradual development of spinal plasticity. The inferior olive, cerebellum, sensorimotor cortex, and corticospinal tract (CST) are essential components of this hierarchy. The neuronal and synaptic mechanisms of the spinal plasticity are under study. Because acquisition of this skill changes the spinal cord, it can affect other skills, such as locomotion. Thus, it enables investigation of how the highly plastic spinal cord supports the acquisition and maintenance of a broad repertoire of motor skills throughout life. These studies have resulted in the negotiated equilibrium model of spinal cord function, which reconciles the spinal cord's long-recognized reliability as the final common pathway for behaviors with its recently recognized ongoing plasticity. In accord with this model, appropriate H-reflex conditioning in a person with spasticity due to an incomplete spinal cord injury can trigger wider beneficial plasticity that markedly improves walking. H-reflex operant conditioning appears to provide a valuable new method for enhancing functional recovery in people with spinal cord injury and possibly other disorders as well.

Acquisition, Maintenance, and Therapeutic Use of a Simple Motor Skill.

Operant conditioning of Hoffmann's reflex (H-reflex) is a non-invasive and targeted therapeutic intervention for patients with movement disorders following spinal cord injury. The reflex-conditioning protocol uses electromyography (EMG) to measure reflexes from specific muscles elicited using transcutaneous electrical stimulation. Despite recent advances in wearable electronics, existing EMG systems that measure muscle activity for operant conditioning of spinal reflexes still use rigid metal electrodes with conductive gels and aggressive adhesives, while requiring precise positioning to ensure reliability of data across experimental sessions. Here, we present the first large-area epidermal electronic system (L-EES) and demonstrate its use in every step of the reflex-conditioning protocol. The L-EES is a stretchable and breathable composite of nanomembrane electrodes (16 electrodes in a four by four array), elastomer, and fabric. The nanomembrane electrode array enables EMG recording from a large surface area on the skin and the breathable elastomer with fabric is biocompatible and comfortable for patients. We show that L-EES can record direct muscle responses (M-waves) and H-reflexes, both of which are comparable to those recorded using conventional EMG recording systems. In addition, L-EES may improve the reflex-conditioning protocol; it has potential to automatically optimize EMG electrode positioning, which may reduce setup time and error across experimental sessions.

Breathable, large-area epidermal electronic systems for recording electromyographic activity during operant conditioning of H-reflex.

We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that does not require the user to choose a feature extraction method or channel set. Instead, the classifier uses multiple feature extraction methods and channel selections to infer the SSVEP and relies on majority voting to pick the most likely target. The classifier extends the window length dynamically if no target obtains the majority of votes. Compared with existing solutions, our classifier: (i) does not assume that any single feature extraction method will consistently outperform the others; (ii) adapts the channel selection to individual users or tasks; (iii) uses dynamic window lengths; (iv) is unsupervised (i.e., does not need training). Collectively, these characteristics make the classifier easy-to-use, especially for caregivers and others with limited technical expertise. We evaluated the performance of our classifier on a publicly available benchmark dataset from 35 healthy participants. We compared the information transfer rate (ITR) of this new classifier to those of the minimum energy combination (MEC), maximum synchronization index (MSI), and filter bank canonical correlation analysis (FBCCA). The new classifier increases average ITR to 123.5 bits-per-minute (bpm), 47.5, 51.2, and 19.5 bpm greater than the MEC, MSI, and FBCCA classifiers, respectively.

A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs.

Present methods for assessing color vision require the person's active participation. Here we describe a brain-computer interface-based method for assessing color vision that does not require the person's participation.This method uses steady-state visual evoked potentials to identify metamers-two light sources that have different spectral distributions but appear to the person to be the same color.We demonstrate that: minimization of the visual evoked potential elicited by two flickering light sources identifies the metamer; this approach can distinguish people with color-vision deficits from those with normal color vision; and this metamer-identification process can be automated.This new method has numerous potential clinical, scientific, and industrial applications.

James J. S. Norton

National Center for Adaptive Neurotechnologies

Stratton VA Medical Center

The Evoked Potential Operant Conditioning System (EPOCS): A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders

James J. S. Norton

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Stratton VA Medical Center

The Evoked Potential Operant Conditioning System (EPOCS): A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders

National Center for Adaptive Neurotechnologies