03130nas a2200313 4500008004100000022001400041245013900055210006900194260001500263520206900278653002002347653002602367653002102393653002202414653001302436653001102449653002702460653002502487100001702512700001702529700001902546700002402565700002302589700002102612700001402633700002402647700002202671856012302693 2022 eng d a1940-087X00aThe Evoked Potential Operant Conditioning System (EPOCS): A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders.0 aEvoked Potential Operant Conditioning System EPOCS A Research To c2022 08 253 a

The Evoked Potential Operant Conditioning System (EPOCS) is a software tool that implements protocols for operantly conditioning stimulus-triggered muscle responses in people with neuromuscular disorders, which in turn can improve sensorimotor function when applied appropriately. EPOCS monitors the state of specific target muscles-e.g., from surface electromyography (EMG) while standing, or from gait cycle measurements while walking on a treadmill-and automatically triggers calibrated stimulation when pre-defined conditions are met. It provides two forms of feedback that enable a person to learn to modulate the targeted pathway's excitability. First, it continuously monitors ongoing EMG activity in the target muscle, guiding the person to produce a consistent level of activity suitable for conditioning. Second, it provides immediate feedback of the response size following each stimulation and indicates whether it has reached the target value. To illustrate its use, this article describes a protocol through which a person can learn to decrease the size of the Hoffmann reflex-the electrically-elicited analog of the spinal stretch reflex-in the soleus muscle. Down-conditioning this pathway's excitability can improve walking in people with spastic gait due to incomplete spinal cord injury. The article demonstrates how to set up the equipment; how to place stimulating and recording electrodes; and how to use the free software to optimize electrode placement, measure the recruitment curve of direct motor and reflex responses, measure the response without operant conditioning, condition the reflex, and analyze the resulting data. It illustrates how the reflex changes over multiple sessions and how walking improves. It also discusses how the system can be applied to other kinds of evoked responses and to other kinds of stimulation, e.g., motor evoked potentials to transcranial magnetic stimulation; how it can address various clinical problems; and how it can support research studies of sensorimotor function in health and disease.

10aChronic Disease10aConditioning, Operant10aElectromyography10aEvoked Potentials10aH-Reflex10aHumans10aNeuromuscular Diseases10aSpinal Cord Injuries1 aHill, Jeremy1 aGupta, Disha1 aEftekhar, Amir1 aBrangaccio, Jodi, A1 aNorton, James, J S1 aMcLeod, Michelle1 aFake, Tim1 aWolpaw, Jonathan, R1 aThompson, Aiko, K uhttps://www.neurotechcenter.org/publications/2022/evoked-potential-operant-conditioning-system-epocs-research-tool-and01696nas a2200289 4500008004100000022001400041245006300055210006000118260001600178490000700194520079100201653003000992653001701022653002701039653003001066653001101096653001001107653002301117653002001140100002301160700002201183700002201205700002201227700002401249700002401273856010901297 2021 eng d a1741-255200aBrain-computer interface-based assessment of color vision.0 aBraincomputer interfacebased assessment of color vision c2021 Nov 260 v183 a

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.

10abrain-computer interfaces10aColor Vision10aElectroencephalography10aEvoked Potentials, Visual10aHumans10aLight10aPhotic Stimulation10aResearch Design1 aNorton, James, J S1 aDiRisio, Grace, F1 aCarp, Jonathan, S1 aNorton, Amanda, E1 aKochan, Nicholas, S1 aWolpaw, Jonathan, R uhttps://www.neurotechcenter.org/publications/2021/brain-computer-interface-based-assessment-color-vision02303nas a2200265 4500008004100000022001400041245007700055210006900132260000900201300001400210490000700224520143600231653001501667653003001682653002701712653003001739653001101769653002301780100002101803700002301824700002401847700001801871700002801889856012001917 2021 eng d a1558-021000aA Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs.0 aVotingEnhanced DynamicWindowLength Classifier for SSVEPBased BCI c2021 a1766-17730 v293 a

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.

10aAlgorithms10abrain-computer interfaces10aElectroencephalography10aEvoked Potentials, Visual10aHumans10aPhotic Stimulation1 aHabibzadeh, Hadi1 aNorton, James, J S1 aVaughan, Theresa, M1 aSoyata, Tolga1 aZois, Daphney-Stavroula uhttps://www.neurotechcenter.org/publications/2021/voting-enhanced-dynamic-window-length-classifier-ssvep-based-bcis02570nas a2200325 4500008004100000022001400041245013800055210006900193260001200262300001100274490000800285520150800293653002601801653002601827653001601853653001301869653001101882653003101893100002001924700002301944700002001967700002001987700001802007700002302025700001902048700001902067700002402086700001902110856011502129 2020 eng d a1873-423500aBreathable, large-area epidermal electronic systems for recording electromyographic activity during operant conditioning of H-reflex.0 aBreathable largearea epidermal electronic systems for recording  c10/2020 a1124040 v1653 a

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.

10aBiosensing Techniques10aConditioning, Operant10aElectronics10aH-Reflex10aHumans10aReproducibility of Results1 aKwon, Young-Tae1 aNorton, James, J S1 aCutrone, Andrew1 aLim, Hyo-Ryoung1 aKwon, Shinjae1 aChoi, Jeongmoon, J1 aKim, Hee, Seok1 aJang, Young, C1 aWolpaw, Jonathan, R1 aYeo, Woon-Hong uhttps://www.neurotechcenter.org/publications/2020/breathable-large-area-epidermal-electronic-systems-recording