@article {4436, title = {The Evoked Potential Operant Conditioning System (EPOCS): A Research Tool and an Emerging Therapy for Chronic Neuromuscular Disorders.}, journal = {J Vis Exp}, year = {2022}, month = {2022 08 25}, abstract = {

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{\textquoteright}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{\textquoteright}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.

}, keywords = {Chronic Disease, Conditioning, Operant, Electromyography, Evoked Potentials, H-Reflex, Humans, Neuromuscular Diseases, Spinal Cord Injuries}, issn = {1940-087X}, doi = {10.3791/63736}, author = {Hill, N Jeremy and Gupta, Disha and Eftekhar, Amir and Brangaccio, Jodi A and Norton, James J S and McLeod, Michelle and Fake, Tim and Wolpaw, Jonathan R and Thompson, Aiko K} } @article {4494, title = {Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier.}, journal = {Brain Comput Interfaces (Abingdon)}, volume = {9}, year = {2022}, month = {2022}, pages = {69-101}, abstract = {

The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9th, 2021 in a virtual format. The conference continued the BCI Meeting series{\textquoteright} interactive nature with 21 workshops covering topics in BCI (also called brain-machine interface) research. As in the past, workshops covered the breadth of topics in BCI. Some workshops provided detailed examinations of specific methods, hardware, or processes. Others focused on specific BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and increase the ease of comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were both the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, presents an overview of the discussion topics, and describes the conclusion, challenges, or initiatives that resulted from the interactions and discussion at the workshop.

}, issn = {2326-263X}, doi = {10.1080/2326263X.2021.2009654}, author = {Huggins, Jane E and Krusienski, Dean and Vansteensel, Mariska J and Valeriani, Davide and Thelen, Antonia and Stavisky, Sergey and Norton, James J S and Nijholt, Anton and M{\"u}ller-Putz, Gernot and Kosmyna, Nataliya and Korczowski, Louis and Kapeller, Christoph and Herff, Christian and Halder, Sebastian and Guger, Christoph and Grosse-Wentrup, Moritz and Gaunt, Robert and Dusang, Aliceson Nicole and Clisson, Pierre and Chavarriaga, Ricardo and Anderson, Charles W and Allison, Brendan Z and Aksenova, Tetiana and Aarnoutse, Erik} } @article {4495, title = {Brain-computer interface-based assessment of color vision.}, journal = {J Neural Eng}, volume = {18}, year = {2021}, month = {2021 Nov 26}, abstract = {

Present methods for assessing color vision require the person{\textquoteright}s active participation. Here we describe a brain-computer interface-based method for assessing color vision that does not require the person{\textquoteright}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.

}, keywords = {brain-computer interfaces, Color Vision, Electroencephalography, Evoked Potentials, Visual, Humans, Light, Photic Stimulation, Research Design}, issn = {1741-2552}, doi = {10.1088/1741-2552/ac3264}, author = {Norton, James J S and DiRisio, Grace F and Carp, Jonathan S and Norton, Amanda E and Kochan, Nicholas S and Wolpaw, Jonathan R} } @article {4362, title = {A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {29}, year = {2021}, month = {2021}, pages = {1766-1773}, abstract = {

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.

}, keywords = {Algorithms, brain-computer interfaces, Electroencephalography, Evoked Potentials, Visual, Humans, Photic Stimulation}, issn = {1558-0210}, doi = {10.1109/TNSRE.2021.3106876}, author = {Habibzadeh, Hadi and Norton, James J S and Vaughan, Theresa M and Soyata, Tolga and Zois, Daphney-Stavroula} } @article {4366, title = {Breathable, large-area epidermal electronic systems for recording electromyographic activity during operant conditioning of H-reflex.}, journal = {Biosens Bioelectron}, volume = {165}, year = {2020}, month = {10/2020}, pages = {112404}, abstract = {

Operant conditioning of Hoffmann{\textquoteright}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.

}, keywords = {Biosensing Techniques, Conditioning, Operant, Electronics, H-Reflex, Humans, Reproducibility of Results}, issn = {1873-4235}, doi = {10.1016/j.bios.2020.112404}, author = {Kwon, Young-Tae and Norton, James J S and Cutrone, Andrew and Lim, Hyo-Ryoung and Kwon, Shinjae and Choi, Jeongmoon J and Kim, Hee Seok and Jang, Young C and Wolpaw, Jonathan R and Yeo, Woon-Hong} }