Functional electrical stimulation therapy controlled by a P300-based brain-computer interface, as a therapeutic alternative for upper limb motor function recovery in chronic post-stroke patients. A non-randomized pilot study.

Introduction: Up to 80% of post-stroke patients present upper-limb motor impairment (ULMI), causing functional limitations in daily activities and loss of independence. UMLI is seldom fully recovered after stroke when using conventional therapeutic approaches. Functional Electrical Stimulation Therapy (FEST) controlled by Brain-Computer Interface (BCI) is an alternative that may induce neuroplastic changes, even in chronic post-stroke patients. The purpose of this work was to evaluate the effects of a P300-based BCI-controlled FEST intervention, for ULMI recovery of chronic post-stroke patients. Methods: A non-randomized pilot study was conducted, including 14 patients divided into 2 groups: BCI-FEST, and Conventional Therapy. Assessments of Upper limb functionality with Action Research Arm Test (ARAT), performance impairment with Fugl-Meyer assessment (FMA), Functional Independence Measure (FIM) and spasticity through Modified Ashworth Scale (MAS) were performed at baseline and after carrying out 20 therapy sessions, and the obtained scores compared using Chi square and Mann-Whitney U statistical tests (𝛼 = 0.05). Results: After training, we found statistically significant differences between groups for FMA (p = 0.012), ARAT (p < 0.001), and FIM (p = 0.025) scales. Discussion: It has been shown that FEST controlled by a P300-based BCI, may be more effective than conventional therapy to improve ULMI after stroke, regardless of chronicity. Conclusion: The results of the proposed BCI-FEST intervention are promising, even for the most chronic post-stroke patients often relegated from novel interventions, whose expected recovery with conventional therapy is very low. It is necessary to carry out a randomized controlled trial in the future with a larger sample of patients.

Thermal Evaluation of Multi-Antenna Systems Proposed to Treat Bone Tumors: Finite Element Analysis.

Microwave ablation is commonly used in soft tissue tumors, but its application in bone tumors has been barely analyzed. Antennas to treat bone tissue (~3 cm2), has been lately designed. Bone tumors at pathological stage T1 can reach 8 cm wide. An antenna cannot cover it; therefore, our goal is to evaluate the thermal performance of multi-antenna arrays. Linear, triangular, and square configurations of double slot (DS) and monopole (MTM) antennas were evaluated. A parametric study (finite element method), with variations in distance between antennas (ad) and bone thickness (bt) was implemented. Array feasibility was evaluated by SWR, ablated tissue volume, etc. The linear configuration with DS and MTM antennas showed SWR ≤ 1.6 for ad = 1 mm−15 mm and bt = 20 mm−40 mm, and ad = 10 mm−15 mm and bt = 25 mm−40 mm, respectively; the triangular showed SWR ≤ 1.5 for ad = 5 mm−15 mm and bt = 20 mm−40 mm and ad = 10 mm−15 mm and bt = 25 mm−40 mm. The square configuration (DS) generated SWR ≤ 1.5 for ad = 5 mm−20 mm and bt = 20 mm−40 mm, and the MTM, SWR ≤ 1.5 with ad = 10 mm and bt = 25 mm−40 mm. Ablated tissue was 4.65 cm3−10.46 cm3 after 5 min. According to treatment time and array configuration, maximum temperature and ablated tissue is modified. Bone tumors >3 cm3 can be treated by these antenna-arrays.

Devices and Technology in Transcranial Magnetic Stimulation: A Systematic Review.

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.

Personalized protocol and scoring scale for functional electrical stimulation of the hand: A pilot feasibility study.

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.

Artificial Intelligence Algorithms in Visual Evoked Potential-Based Brain-Computer Interfaces for Motor Rehabilitation Applications: Systematic Review and Future Directions.

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.

Anatomical 3D Modeling Using IR Sensors and Radiometric Processing Based on Structure from Motion: Towards a Tool for the Diabetic Foot Diagnosis.

Medical infrared thermography has proven to be a complementary procedure to physiological disorders, such as the diabetic foot. However, the technique remains essentially based on 2D images that display partial anatomy. In this context, a 3D thermal model provides improved visualization and faster inspection. This paper presents a 3D reconstruction method associated with temperature information. The proposed solution is based on a Structure from Motion and Multi-view Stereo approach, exploiting a set of multimodal merged images. The infrared images were obtained by automatically processing the radiometric data to remove thermal interferences, segment the RoI, enhance false-color contrast, and for multimodal co-registration under a controlled environment and a ∆T < 2.6% between the RoI and thermal interferences. The geometric verification accuracy was 77% ± 2%. Moreover, a normalized error was adjusted per sample based on a linear model to compensate for the curvature emissivity (error ≈ 10% near to 90°). The 3D models were displayed with temperature information and interaction controls to observe any point of view. The temperature sidebar values were assigned with information retrieved only from the RoI. The results have proven the feasibility of the 3D multimodal construction to be used as a promising tool in the diagnosis of diabetic foot.

A Flexible Pulse Generator Based on a Field Programmable Gate Array Architecture for Functional Electrical Stimulation.

Non-invasive Functional Electrical Stimulation (FES) is a technique applied for motor rehabilitation of patients with central nervous system injury. This technique requires programmable multichannel systems to configure the stimulation parameters (amplitude, frequency, and pulse width). Most FES systems are based on microcontrollers with fixed architecture; this limits the control of the parameters and the scaling to multiple channels. Although field programmable gate arrays (FPGA) have been used in FES systems as alternative to microcontrollers, most of them focus on signal acquisition, processing, or communication functions, or are for invasive stimulation. A few FES systems report using FPGAs for parameter configuration and pulse generation in non-invasive FES. However, generally they limit the value of the frequency or amplitude parameters to enable multichannel operation. This restricts free selection of parameters and implementation of modulation patterns, previously reported to delay FES-induced muscle fatigue. To overcome those limitations, this paper presents a proof-of-concept (technology readiness level three-TRL 3) regarding the technical feasibility and potential use of an FPGA-based pulse generator for non-invasive FES applications (PG-nFES). The main aims were: (1) the development of a flexible pulse generator for FES applications and (2) to perform a proof-of-concept of the system, comprising: electrical characterization of the stimulation parameters, and verification of its potential for upper limb FES applications. Biphasic stimulation pulses with high linearity (r 2 > 0.9998) and repeatability (>0.81) were achieved by combining the PG-nFES with a current-controlled output stage. Average percentage error in the characterizations was under 3% for amplitude (1-48 mA) and pulse width (20-400 µs), and 0% for frequency (10-150 Hz). A six-channel version of the PG-nFES was implemented to demonstrate the scalability feature. The independence of parameters was tested with three patterns of co-modulation of two parameters. Moreover, two complete FES channels were implemented and the claimed features of the PG-nFES were verified by performing upper limb functional movements involving the hand and the arm. Finally, the system enabled implementation of a stimulation pattern with co-modulation of frequency and pulse width, applied successfully for efficient elbow during repetitions of a functional movement.

Computer-aided diagnosis in rheumatic diseases using ultrasound: an overview.

Clinical evaluation of rheumatic and musculoskeletal diseases through images is a challenge for the beginner rheumatologist since image diagnosis is an expert task with a long learning curve. The aim of this work was to present a narrative review on the main ultrasound computer-aided diagnosis systems that may help clinicians thanks to the progress made in the application of artificial intelligence techniques. We performed a literature review searching for original articles in seven repositories, from 1970 to 2019, and identified 11 main methods currently used in ultrasound computer-aided diagnosis systems. Also, we found that rheumatoid arthritis, osteoarthritis, systemic lupus erythematosus, and idiopathic inflammatory myopathies are the four musculoskeletal and rheumatic diseases most studied that use these innovative systems, with an overall accuracy of > 75%.

Motor Imagery-Based Brain-Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients.

Motor imagery-based brain-computer interfaces (BCI) have shown potential for the rehabilitation of stroke patients; however, low performance has restricted their application in clinical environments. Therefore, this work presents the implementation of a BCI system, coupled to a robotic hand orthosis and driven by hand motor imagery of healthy subjects and the paralysed hand of stroke patients. A novel processing stage was designed using a bank of temporal filters, the common spatial pattern algorithm for feature extraction and particle swarm optimisation for feature selection. Offline tests were performed for testing the proposed processing stage, and results were compared with those computed with common spatial patterns. Afterwards, online tests with healthy subjects were performed in which the orthosis was activated by the system. Stroke patients' average performance was 74.1 ± 11%. For 4 out of 6 patients, the proposed method showed a statistically significant higher performance than the common spatial pattern method. Healthy subjects' average offline and online performances were of 76.2 ± 7.6% and 70 ± 6.7, respectively. For 3 out of 8 healthy subjects, the proposed method showed a statistically significant higher performance than the common spatial pattern method. System's performance showed that it has a potential to be used for hand rehabilitation of stroke patients.

A Dependable Massive Storage Service for Medical Imaging.

We present the construction of Babel, a distributed storage system that meets stringent requirements on dependability, availability, and scalability. Together with Babel, we developed an application that uses our system to store medical images. Accordingly, we show the feasibility of our proposal to provide an alternative solution for massive scientific storage and describe the software architecture style that manages the DICOM images life cycle, utilizing Babel like a virtual local storage component for a picture archiving and communication system (PACS-Babel Interface). Furthermore, we describe the communication interface in the Unified Modeling Language (UML) and show how it can be extended to manage the hard work associated with data migration processes on PACS in case of updates or disaster recovery.

sEMG Signal Acquisition Strategy towards Hand FES Control.

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.

An Application for Skin Macules Characterization Based on a 3-Stage Image-Processing Algorithm for Patients with Diabetes.

Diabetic skin manifestations, previous to ulcers and wounds, are not highly accounted as part of diagnosis even when they represent the first symptom of vascular damage and are present in up to 70% of patients with diabetes mellitus type II. Here, an application for skin macules characterization based on a three-stage segmentation and characterization algorithm used to classify vascular, petechiae, trophic changes, and trauma macules from digital photographs of the lower limbs is presented. First, in order to find the skin region, a logical multiplication is performed on two skin masks obtained from color space transformations; dynamic thresholds are stabilised to self-adjust to a variety of skin tones. Then, in order to locate the lesion region, illumination enhancement is performed using a chromatic model color space, followed by a principal component analysis gray-scale transformation. Finally, characteristics of each type of macule are considered and classified; morphologic properties (area, axes, perimeter, and solidity), intensity properties, and a set of shade indices (red, green, blue, and brown) are proposed as a measure to obviate skin color differences among subjects. The values calculated show differences between macules with a statistical significance, which agree with the physician's diagnosis. Later, macule properties are fed to an artificial neural network classifier, which proved a 97.5% accuracy, to differentiate between them. Characterization is useful in order to track macule changes and development along time, provides meaningful information to provide early treatments, and offers support in the prevention of amputations due to diabetic feet. A graphical user interface was designed to show the properties of the macules; this application could be the background of a future Diagnosis Assistance Tool for educational (i.e., untrained physicians) and preventive assistance technology purposes.

Gender Differences in Quantitative Electroencephalogram During a Simple Hand Movement Task in Young Adults.

BACKGROUND: No consensus has been reached regarding the existence of gender differences during motor tasks in electroencephalography. This could lead to misinterpretation of electroencephalography clinical diagnosis and affect the calibration of brain-computer interfaces. OBJECTIVE: To assess whether there are statistically significant gender differences in electroencephalography recorded during hand movements. METHODS: Electroencephalography data were recorded from 18 women and 18 men while performing hand movements and rest. Electroencephalography power was computed for alpha (8-13 Hz), beta (14-30 Hz), and a broader band including alpha and beta (8-30 Hz) using wavelet transform. Statistical analysis was done using a General Linear Model for repeated measurements (α = 0.05). Additionally, topographic maps were computed for each gender. RESULTS: Significant gender differences were found for the rest condition in all analyzed bands. For the hand movement tasks, gender differences were mainly found in the beta band and located in temporoparietal areas. Power decrease observed in topographic maps was located in the centro-parietal areas for females and the centro-frontal areas for males. Additionally, greater power decreases were observed for women in all analyzed frequency bands. CONCLUSION: Electroencephalography parameters used for the diagnosis of neuromotor diseases, as well as for brain-computer interface calibration, must take gender into account.

Proposal and Evaluation of a Telerehabilitation Platform Designed for Patients With Partial Rotator Cuff Tears: A Preliminary Study.

OBJECTIVE: To propose and evaluate the effectiveness of a telerehabilitation platform designed for patients with rotator cuff (RC) tears. METHODS: During the first study phase, a virtual service platform that included information on RC tear pathology, joint care, and a series of instructions regarding therapeutic exercise was designed and created. Subsequently, in the clinical phase, a quasi-experimental study was performed. The platform was tested on patients and evaluated at baseline and at 1, 2, 3, and 6 months with respect to their pain levels and functionality on the Constant-Murley (CM) scale. RESULTS: Eleven patients were included, 5 women and 6 men, with a median age of 55 years (range, 42-68 years). Pain diminished from a baseline value of 64 mm (range, 40-80 mm) to 16 mm (range, 0-30 mm) at 6 months (p<0.001). Points on the CM scale rose from a baseline value of 54 points (range, 51-66 points) to 85 points (range, 70-100 points) at 6 months (p=0.001). Functionality in daily living and work activities, movement, and strength exhibited significant changes at 6 months (p<0.05). CONCLUSION: Significant changes were observed in pain and functionality in this group of participants who used a telerehabilitation platform. To the best of our knowledge, this is the first study that included a specific program for RC tears.

Business Model for the Security of a Large-Scale PACS, Compliance with ISO/27002:2013 Standard.

Data security is a critical issue in an organization; a proper information security management (ISM) is an ongoing process that seeks to build and maintain programs, policies, and controls for protecting information. A hospital is one of the most complex organizations, where patient information has not only legal and economic implications but, more importantly, an impact on the patient's health. Imaging studies include medical images, patient identification data, and proprietary information of the study; these data are contained in the storage device of a PACS. This system must preserve the confidentiality, integrity, and availability of patient information. There are techniques such as firewalls, encryption, and data encapsulation that contribute to the protection of information. In addition, the Digital Imaging and Communications in Medicine (DICOM) standard and the requirements of the Health Insurance Portability and Accountability Act (HIPAA) regulations are also used to protect the patient clinical data. However, these techniques are not systematically applied to the picture and archiving and communication system (PACS) in most cases and are not sufficient to ensure the integrity of the images and associated data during transmission. The ISO/IEC 27001:2013 standard has been developed to improve the ISM. Currently, health institutions lack effective ISM processes that enable reliable interorganizational activities. In this paper, we present a business model that accomplishes the controls of ISO/IEC 27002:2013 standard and criteria of security and privacy from DICOM and HIPAA to improve the ISM of a large-scale PACS. The methodology associated with the model can monitor the flow of data in a PACS, facilitating the detection of unauthorized access to images and other abnormal activities.

An approach to improve the performance of subject-independent BCIs-based on motor imagery allocating subjects by gender.

BACKGROUND: One of the difficulties for the implementation of Brain-Computer Interface (BCI) systems for motor impaired patients is the time consumed in the system design process, since patients do not have the adequate physical nor psychological conditions to complete the process. For this reason most of BCIs are designed in a subject-dependent approach using data of healthy subjects. The developing of subject-independent systems is an option to decrease the required training sessions to design a BCI with patient functionality. This paper presents a proof-of-concept study to evaluate subject-independent system based on hand motor imagery taking gender into account. METHODS: Subject-Independent BCIs are proposed using Common Spatial Patterns and log variance features of two groups of healthy subjects; one of the groups was composed by people of male gender and the other one by people of female gender. The performance of the developed gender-specific BCI designs was evaluated with respect to a subject-independent BCI designed without taking gender into account, and afterwards its performance was evaluated with data of two healthy subjects that were not included in the initial sample. As an additional test to probe the potential use for subcortical stroke patients we applied the methodology to two patients with right hand weakness. T-test was employed to determine the significance of the difference between traditional approach and the proposed gender-specific approach. RESULTS: For most of the tested conditions, the gender-specific BCIs have a statistically significant better performance than those that did not take gender into account. It was also observed that with a BCI designed with log-variance features in the alpha and beta band of healthy subjects' data, it was possible to classify hand motor imagery of subcortical stroke patients above the practical level of chance. CONCLUSIONS: A larger subjects' sample test may be necessary to improve the performances of the gender-specific BCIs and to further test this methodology on different patients. The reduction of complexity in the implementation of BCI systems could bring these systems closer to applications such as controlling devices for the motor rehabilitation of stroke patients, and therefore, contribute to a more effective neurological rehabilitation.

[Characterization of electrical brain activity related to hand motor imagery in healthy subjects]. / Caracterización de la actividad eléctrica cerebral relacionada con la imaginacián del movimiento de la mano en sujetos sanos.

Brain computer interface systems (BCI) translate the intentions of patients affected with locked-in syndrome through the EEG signal characteristics, which are converted into commands used to control external devices. One of the strategies used, is to decode the motor imagery of the subject, which can modify the neuronal activity in the sensory-motor areas in a similar way to which it is observed in real movement. The present study shows the activation patterns that are registered in motor and motor imagery tasks of right and left hand movement in a sample of young healthy subjects of Mexican nationality. By means of frequency analysis it was possible to determine the difference conditions of motor imagery and movement. Using U Mann- Whitney tests, differences with statistical significance (p < 0.05) where obtained, in the EEG channels C3, Cz, C4, T3 and P3 in the mu and beta rhythms, for subjects with similar characteristics (age, gender, and education). With these results, it would be possible to define a classifier or decoder by gender that improves the performance rate and diminishes the training time, with the goal of designing a functional BCI system that can be transferred from the laboratory to the clinical application in patients with motor disabilities.

System to measure the range of motion of the joints of the human hand .

Today, the manual goniometer is a common tool used in clinical practice to measure the range of motion (ROM) of joints in the hand. This device is not only tedious and highly timeconsuming to use, but its accuracy mainly depends on the experience of the examiner. The majority of electronic goniometers currently available on the market exhibit these same limitations. This document presents the physical design and attributes of a system known as the MULTIELGON, which can be used automatically to obtain multiple measurements of the angles of the hand's small finger joints simultaneously using a novel angle-to-voltage transducer. The reproducibility and repeatability of the transducer were evaluated; low dispersion and high homogeneity were demonstrated. Correlation and Bland-Altman analyses were used to compare the accuracy of the novel transducer (A) and traditional manual goniometer (B); the correlation coefficient was 0.9995. The Bland-Altman analysis determined the limits of agreement (1.4° to 1.7°) with a 95% confidence interval for any variation between the instruments (A and B), which gave readings differing by less than 3.1°. Differences were sufficiently small to propose that the manual goniometer can be replaced by the transducer; moreover A is best to evaluate the hand's small finger joints than B. The system is comprised of the device, the interface and the MULTIELGON GUI. The device consists of 14 angle-to-voltage transducers that can be attached to joints in the hand using a PVC clamp and an elastic glove. The MULTIELGON can be utilized to evaluate patients, as well as record and manage ROM data for surgical and rehabilitation decisions.

[Technological advances in neurorehabilitation]. / Avances tecnológicos en neurorrehabilitación.

Neurological rehabilitation arose as formal method in the 60's, for the therapeutic treatment of patients with stroke or spinal cord injury, which develop severe sequelae that affect their motor and sensory abilities. Although the Central Nervous System has plasticity mechanisms for spontaneous recovery, a high percentage of patients should receive specialized therapies to regain motor function, such as Constraint Induced Movement Therapy or Upright physical Therapy. The neurorehabilitation has undergone drastic changes over the last two decades due to the incorporation of computer and robotic electronic devices, designed to produce positive changes in cortical excitability of the cerebral hemisphere damaged and so to improve neuroplasticity. Among equipment, we can mention those for electrotherapy devices, apparatus for transcranial magnetic stimulation, the robotic lower limb orthoses, robot for upper limb training, systems for functional electrical stimulation, neuroprosthesis and brain computer interfaces. These devices have caused controversy because of its application and benefits reported in the literature. The aim of Neurorehabilitation technologies is to take advantage of the functional neuromuscular structures preserved, and they compensate or re-learn the functions that previously made the damaged areas. The purpose of this article is to mention some clinical applications and benefits that these technologies offer to patients with neuronal injury.