01902nas a2200241   4500008004100000245011900041210006900160300000800229490000800237520107300245100002401318700001701342700002101359700002501380700001301405700002201418700002301440700002401463700002201487700002101509700001101530856011901541       2025                        eng d00aSoleus H-reflex size versus stimulation rate in the presence of background muscle activity: a methodological study0 aSoleus Hreflex size versus stimulation rate in the presence of b  a2150 v2433 aHoffmann reflex (HR) operant conditioning (HROC) is an important intervention for neurorehabilitation. Current HROC paradigms elicit HRs at low rates (~ 0.2 Hz), minimizing rate-dependent depression (RDD). We investigated the impact of higher stimulation rates on HR size. Fifteen healthy participants maintained low background soleus electromyographic activity (EMG) while standing. Soleus HR and M-wave recruitment curves were obtained at rates of 0.2, 1, and 2 Hz twice, from which Mmax and Hmax were calculated. Seventy-five HRs were collected for each rate at a target M-wave size (~ 10 to 20% of Mmax). HR depression was minimal at higher stimulation rates. The mean HR amplitude was reliable across the two repetitions and three rates, with high intraclass correlation coefficient (ICC) values. HROC could be performed consistently at rates up to 2 Hz with minimal HR depression. Faster rates enable more conditioning trials per session, reducing session duration and/or number, thereby potentially accelerating conditioning and reducing participant burden.1 aBrangaccio, Jodi, A1 aGupta, Disha1 aMojtabavi, Helia1 aHardesty, Russell, L1 aHill, NJ1 aCarp, Jonathan, S1 aGemoets, Darren, E1 aVaughan, Theresa, M1 aNorton, James, JS1 aPerez, Monica, A1 aothers  uhttps://www.neurotechcenter.org/publications/2025/soleus-h-reflex-size-versus-stimulation-rate-presence-background02579nas a2200289   4500008004100000022001400041245014200055210006900197260001200266300001400278490000800292520157400300653003401874653003001908653002701938653001101965653002201976653001101998100002802009700001602037700002502053700002102078700002102099700002402120700002402144856012102168       2023                        eng d  a1432-145900aTowards clinical application of implantable brain-computer interfaces for people with late-stage ALS: medical and ethical considerations.0 aTowards clinical application of implantable braincomputer interf  c03/2023  a1323-13360 v2703 a<p>Individuals with amyotrophic lateral sclerosis (ALS) frequently develop speech and communication problems in the course of their disease. Currently available augmentative and alternative communication technologies do not present a solution for many people with advanced ALS, because these devices depend on residual and reliable motor activity. Brain-computer interfaces (BCIs) use neural signals for computer control and may allow people with late-stage ALS to communicate even when conventional technology falls short. Recent years have witnessed fast progression in the development and validation of implanted BCIs, which place neural signal recording electrodes in or on the cortex. Eventual widespread clinical application of implanted BCIs as an assistive communication technology for people with ALS will have significant consequences for their daily life, as well as for the clinical management of the disease, among others because of the potential interaction between the BCI and other procedures people with ALS undergo, such as tracheostomy. This article aims to facilitate responsible real-world implementation of implanted BCIs. We review the state of the art of research on implanted BCIs for communication, as well as the medical and ethical implications of the clinical application of this technology. We conclude that the contribution of all BCI stakeholders, including clinicians of the various ALS-related disciplines, will be needed to develop procedures for, and shape the process of, the responsible clinical application of implanted BCIs.</p>10aAmyotrophic Lateral Sclerosis10abrain-computer interfaces10aElectroencephalography10aHumans10aSelf-Help Devices10aSpeech1 aVansteensel, Mariska, J1 aKlein, Eran1 avan Thiel, Ghislaine1 aGaytant, Michael1 aSimmons, Zachary1 aWolpaw, Jonathan, R1 aVaughan, Theresa, M  uhttps://www.neurotechcenter.org/publications/2023/towards-clinical-application-implantable-brain-computer-interfaces02303nas 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<p>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.</p>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-bcis