Due to damage of the nervous system, patients experience impediments in their daily life: severe fatigue, tremor or impaired hand dexterity, hemiparesis, or hemiplegia. Surface electromyography (sEMG) signal analysis is used to identify motion; however, standardization of electrode placement and classification of sEMG patterns are major challenges. This paper describes a technique used to acquire sEMG signals for five hand motion patterns from six able-bodied subjects using an array of recording and stimulation electrodes placed on the forearm and its effects over functional electrical stimulation (FES) and volitional sEMG combinations, in order to eventually control a sEMG-driven FES neuroprosthesis for upper limb rehabilitation. A two-part protocol was performed. First, personalized templates to place eight sEMG bipolar channels were designed; with these data, a universal template, called forearm electrode set (FELT), was built. Second, volitional and evoked movements were recorded during FES application. 95% classification accuracy was achieved using two sessions per movement. With the FELT, it was possible to perform FES and sEMG recordings simultaneously. Also, it was possible to extract the volitional and evoked sEMG from the raw signal, which is highly important for closed-loop FES control.

Background. A neuroprosthesis (NP) is a medical device that compensates and restores functionality of neural dysfunctions affected by different pathologies and conditions. To this end, an implantable NP (INP) must monitor and electrically stimulate neuronal small structures in the peripheral and central nervous system. Therefore, one of the most important parts of INPs are the sensors and electrodes since their size, resolution, and material are key for their design and performance. Currently, most of the studies focus only on the INP application but do not show the technical considerations of the sensors. Objective. This paper is a systematic literature review that summarizes and synthesizes implantable micro- and nanosensors/electrodes used in INPs for sensing and stimulating tissues. Data Sources. Articles and patents published in English were searched from electronic databases. No restrictions were made in terms of country or journal. Study Selection. All reports related to sensors/electrodes applied in INPs were included, focusing on micro- and nanotechnologies. Main Outcome Measures. Performance and potential profit. Results. There was a total of 153 selected articles from the 2010 to June 2020 period, of which 16 were about cardiac pacemakers, 15 cochlear implants, 13 retinal prosthesis, 31 deep brain stimulation, 6 bladder implants, and 18 implantable motor NPs. All those INPs are used for support or recovery of neural functions for hearing, seeing, pacing, and motor control, as well as bladder and bowel control. Micro- and nanosensors for signal stimulation and recording have four special requirements to meet: biocompatibility, long-term reliability, high selectivity, and low-energy consumption. Current and future considerations in sensor/electrode design should focus on improving efficiency and safety. This review is a first approximation for those who work on INP design; it offers an idea of the complexity on the matter and can guide them to specific references on the subject.

Virtual reality (VR) and augmented reality (AR) are engaging interfaces that can be of benefit for rehabilitation therapy. However, they are still not widely used, and the use of surface electromyography (sEMG) signals is not established for them. Our goal is to explore whether there is a standardized protocol towards therapeutic applications since there are not many methodological reviews that focus on sEMG control/feedback. A systematic literature review using the PRISMA (preferred reporting items for systematic reviews and meta-analyses) methodology is conducted. A Boolean search in databases was performed applying inclusion/exclusion criteria; articles older than 5 years and repeated were excluded. A total of 393 articles were selected for screening, of which 66.15% were excluded, 131 records were eligible, 69.46% use neither VR/AR interfaces nor sEMG control; 40 articles remained. Categories are, application: neurological motor rehabilitation (70%), prosthesis training (30%); processing algorithm: artificial intelligence (40%), direct control (20%); hardware: Myo Armband (22.5%), Delsys (10%), proprietary (17.5%); VR/AR interface: training scene model (25%), videogame (47.5%), first-person (20%). Finally, applications are focused on motor neurorehabilitation after stroke/amputation; however, there is no consensus regarding signal processing or classification criteria. Future work should deal with proposing guidelines to standardize these technologies for their adoption in clinical practice.

Brain-Computer Interface (BCI) is a technology that uses electroencephalographic (EEG) signals to control external devices, such as Functional Electrical Stimulation (FES). Visual BCI paradigms based on P300 and Steady State Visually Evoked potentials (SSVEP) have shown high potential for clinical purposes. Numerous studies have been published on P300- and SSVEP-based non-invasive BCIs, but many of them present two shortcomings: (1) they are not aimed for motor rehabilitation applications, and (2) they do not report in detail the artificial intelligence (AI) methods used for classification, or their performance metrics. To address this gap, in this paper the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to prepare a systematic literature review (SLR). Papers older than 10 years, repeated or not related to a motor rehabilitation application, were excluded. Of all the studies, 51.02% referred to theoretical analysis of classification algorithms. Of the remaining, 28.48% were for spelling, 12.73% for diverse applications (control of wheelchair or home appliances), and only 7.77% were focused on motor rehabilitation. After the inclusion and exclusion criteria were applied and quality screening was performed, 34 articles were selected. Of them, 26.47% used the P300 and 55.8% the SSVEP signal. Five applications categories were established: Rehabilitation Systems (17.64%), Virtual Reality environments (23.52%), FES (17.64%), Orthosis (29.41%), and Prosthesis (11.76%). Of all the works, only four performed tests with patients. The most reported machine learning (ML) algorithms used for classification were linear discriminant analysis (LDA) (48.64%) and support vector machine (16.21%), while only one study used a deep learning algorithm: a Convolutional Neural Network (CNN). The reported accuracy ranged from 38.02 to 100%, and the Information Transfer Rate from 1.55 to 49.25 bits per minute. While LDA is still the most used AI algorithm, CNN has shown promising results, but due to their high technical implementation requirements, many researchers do not justify its implementation as worthwile. To achieve quick and accurate online BCIs for motor rehabilitation applications, future works on SSVEP-, P300-based and hybrid BCIs should focus on optimizing the visual stimulation module and the training stage of ML and DL algorithms.

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The technology for transcranial magnetic stimulation (TMS) has significantly changed over the years, with important improvements in the signal generators, the coils, the positioning systems, and the software for modeling, optimization, and therapy planning. In this systematic literature review (SLR), the evolution of each component of TMS technology is presented and analyzed to assess the limitations to overcome. This SLR was carried out following the PRISMA 2020 statement. Published articles of TMS were searched for in four databases (Web of Science, PubMed, Scopus, IEEE). Conference papers and other reviews were excluded. Records were filtered using terms about TMS technology with a semi-automatic software; articles that did not present new technology developments were excluded manually. After this screening, 101 records were included, with 19 articles proposing new stimulator designs (18.8%), 46 presenting or adapting coils (45.5%), 18 proposing systems for coil placement (17.8%), and 43 implementing algorithms for coil optimization (42.6%). The articles were blindly classified by the authors to reduce the risk of bias. However, our results could have been influenced by our research interests, which would affect conclusions for applications in psychiatric and neurological diseases. Our analysis indicates that more emphasis should be placed on optimizing the current technology with a special focus on the experimental validation of models. With this review, we expect to establish the base for future TMS technological developments.

BACKGROUND: Complex personalized Functional Electrical Stimulation (FES) protocols for calibrating parameters and electrode positioning have been proposed, most being time-consuming or technically cumbersome for clinical settings. Therefore, there is a need for new personalized FES protocols that generate comfortable, functional hand movements, while being feasible for clinical translation. OBJECTIVE: To develop a personalized FES protocol, comprising electrode placement and parameter selection, to generate hand opening (HO), power grasp (PW) and precision grip (PG) movements, and compare in a pilot feasibility study its performance to a non-personalized protocol based on standard FES guidelines. METHODS: Two FES protocols, one personalized (P1) and one non-personalized (P2), were used to produce hand movements in twenty-three healthy participants. FES-induced movements were assessed with a new scoring scale which comprises items for selectivity, functionality, and comfort. RESULTS: Higher FES-HSS scores were obtained with P1 for all movements: HO (p= 0.00013), PW (p= 0.00007), PG (p= 0.00460). Electrode placement time was significantly shorter for P2 (p= 0.00003). Comfort scores were similar for both protocols. CONCLUSIONS: The personalized protocol for electrode placement and parameter selection enabled functional FES-induced hand movements and presented advantages over a non-personalized protocol. This protocol warrants further investigation to confirm its suitability for developing upper-limb rehabilitation interventions with clinical translational potential.