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Rheumatoid arthritis (RA) is an autoimmune disorder that typically affects people between 23 and 60 years old causing chronic synovial inflammation, symmetrical polyarthritis, destruction of large and small joints, and chronic disability. Clinical diagnosis of RA is stablished by current ACR-EULAR criteria, and it is crucial for starting conventional therapy in order to minimize damage progression. The 2010 ACR-EULAR criteria include the presence of swollen joints, elevated levels of rheumatoid factor or anti-citrullinated protein antibodies (ACPA), elevated acute phase reactant, and duration of symptoms. In this paper, a computer-aided system for helping in the RA diagnosis, based on quantitative and easy-to-acquire variables, is presented. The participants in this study were all female, grouped into two classes: class I, patients diagnosed with RA (n = 100), and class II corresponding to controls without RA (n = 100). The novel approach is constituted by the acquisition of thermal and RGB images, recording their hand grip strength or gripping force. The weight, height, and age were also obtained from all participants. The color layout descriptors (CLD) were obtained from each image for having a compact representation. After, a wrapper forward selection method in a range of classification algorithms included in WEKA was performed. In the feature selection process, variables such as hand images, grip force, and age were found relevant, whereas weight and height did not provide important information to the classification. Our system obtains an AUC ROC curve greater than 0.94 for both thermal and RGB images using the RandomForest classifier. Thirty-eight subjects were considered for an external test in order to evaluate and validate the model implementation. In this test, an accuracy of 94.7% was obtained using RGB images; the confusion matrix revealed our system provides a correct diagnosis for all participants and failed in only two of them (5.3%).

A method for performing 3D motion tracking of the shoulder joint with respect to the thorax, using MARG sensors and a data fusion algorithm, is proposed. Two tests were done: (1) qualitative and quantitative analysis of the response of the sensors, static position and during motion, with and without the proposed data fusion algorithm; (2) motion tracking of the shoulder joint with the upper arm, the thorax, and the shoulder joint respect to the thorax. Qualitative analysis of experimental results showed that despite slight variations regarding the evaluated motion, these variations did not have repercussions on the estimated orientation. Quantitative analysis showed that the estimated orientation did not exhibit significant variations, in five minutes, such as drift errors (about 0.1° in static position and less than 1.8° during motion), variations due to noise or magnetic disturbances (RMSE less than 0.04° static position and less than 1° during motion); no singularity problems were reported. The main contributions of this research are a multisensor data fusion algorithm, which combines the complementary properties of gyroscopes, accelerometers, and magnetometers in order to estimate the 3D orientation of two body segments separately and with respect to another body segment considering the spatial relationship between them; and a method for performing 3D motion tracking of two body segments, based on the estimation of their orientation, including motion compensation. The proposed method is applicable to monitoring devices based on IMU/MARG sensors; the performance was evaluated using a customized motion analysis system.

Stroke is a leading cause of motor disability worldwide. Upper limb rehabilitation is particularly challenging since approximately 35% of patients recover significant hand function after 6 months of the stroke’s onset. Therefore, new therapies, especially those based on brain-computer interfaces (BCI) and robotic assistive devices, are currently under research. Electroencephalography (EEG) acquired brain rhythms in alpha and beta bands, during motor tasks, such as motor imagery/intention (MI), could provide insight of motor-related neural plasticity occurring during a BCI intervention. Hence, a longitudinal analysis of subacute stroke patients’ brain rhythms during a BCI coupled to robotic device intervention was performed in this study. Data of 9 stroke patients were acquired across 12 sessions of the BCI intervention. Alpha and beta event-related desynchronization/synchronization (ERD/ERS) trends across sessions and their association with time since stroke onset and clinical upper extremity recovery were analyzed, using correlation and linear stepwise regression, respectively. More EEG channels presented significant ERD/ERS trends across sessions related with time since stroke onset, in beta, compared to alpha. Linear models implied a moderate relationship between alpha rhythms in frontal, temporal, and parietal areas with upper limb motor recovery and suggested a strong association between beta activity in frontal, central, and parietal regions with upper limb motor recovery. Higher association of beta with both time since stroke onset and upper limb motor recovery could be explained by beta relation with closed-loop communication between the sensorimotor cortex and the paralyzed upper limb, and alpha being probably more associated with motor learning mechanisms. The association between upper limb motor recovery and beta activations reinforces the hypothesis that broader regions of the cortex activate during movement tasks as a compensatory mechanism in stroke patients with severe motor impairment. Therefore, EEG across BCI interventions could provide valuable information for prognosis and BCI cortical activity targets.

Background: Stroke is the third cause of disability and the first cause of motor impairment of the upper limb, which significantly reduces independence in activities of daily living. Therefore, the development and use of new therapeutic approaches is needed. Brain-computer interfaces (BCI) based on movement intention (MI) have the potential to improve hand function and brain plasticity after stroke. Purpose: Improve hand function after stroke with BCI with MI. Methods: 8 patients with subacute stroke, left or right hemiparesis was divided in two groups. The first received conventional therapy followed by BCI-MI therapy and the second was given BCI-MI therapy and then conventional therapy. The outcome variables were sensory-motor recovery, hand function, spasticity, level of disability, grip strength, change in motor threshold and amplitude of motor evoked potentials (MEP) and changes in desynchronization/synchronization event-related (EDR/EDS). Results: 4 patients were female, the average age 56 ± 12.7 years, and 87% were ischemic stroke. No differences were found between post-conventional therapy and postBCI-MI in the sensorimotor recovery, the spasticity, and the degree of deficiency in the strength. Only in hand function measured with the Action Research Arm Test (ARAT), a statistically significant difference was found in the BCI-MI therapy group (p= 0.0247). Dynamometry grip strength had a tendency to increase the Post-BCI group. A MEP was obtained in the ipsilesional primary motor cortex of 3 patients. A higher EDR was found in the injured hemisphere in favor of the post-BCI group (p= 0.0165). Conclusions: This study shows that BCI-MI rehabilitation, which combines MI with a robotic hand orthosis as feedback, has a significant improvement in hand function and a greater ERD in the ipsilesional primary motor cortex. It is necessary to carry out controlled clinical trials with a larger sample and with a greater frequency and duration of intervention to measure hand motor and functional improvement of patients after stroke.

Objective. This study assesses upper limb recovery prognosis after stroke with solely physiological information, which can provide an objective estimation of recovery. Approach. Clinical recovery was forecasted using EEG-derived Event-Related Desynchronization/Synchronization and coherence, in addition to Transcranial Magnetic Stimulation elicited motor-evoked potentials and upper limb grip and pinch strength. A Regression Tree Ensemble predicted clinical recovery of a stroke database (n = 10) measured after a two-month intervention with the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE) and the Action Research Arm Test (ARAT). Main results. There were no significant differences between predicted and actual outcomes with FMA-UE (p = 0.29) and ARAT (p = 0.5). Median prediction error for FMA-UE and ARAT were of 0.3 (IQR = 6.2) and 3.4 (IQR = 9.4) points, respectively. Predictions with the most pronounced errors were due to an underestimation of high upper limb recovery. The best features for FMA-UE prediction included mostly beta activity over the sensorimotor cortex. Best ARAT prediction features were cortical beta activity, corticospinal tract integrity of the unaffected hemisphere, and upper limb strength. Significance. Results highlighted the importance of measuring cortical activity related to motor control processes, the unaffected hemisphere’s integrity, and upper limb strength for prognosis. It was also implied that stroke upper limb recovery prediction is feasible using solely physiological variables with a Regression Tree Ensemble, which can also be used to analyze physiological relationships with recovery.

Brain-Computer Interfaces (BCI) decode users’ intentions from the central nervous system and could be applied for upper limb motor rehabilitation of patients that have suffered stroke, one of the main causes of disability worldwide. Despite that research groups have reported the efficacy of these systems, a consensus has not yet been reached regarding their true potential. For this reason, a review of up-to-date assessments of BCI for upper limb stroke rehabilitation is presented from the perspective of analyzing common and different design variables presented across studies. Clinical and pilot studies with a control group were included in the review. Most BCI interventions assessments were performed with robotic assistive devices as feedback, followed by neuromuscular electrical stimulation (NMES) and visual feedbacks. Compared to other experimental interventions, the effects of a BCI intervention have been reported in a low number of patients. In addition, high variability between studies’ designs such as stroke etiology and interventions’ duration, do not allow to assess the potential of BCI for stroke rehabilitation. However, a trend towards significant rehabilitation outcomes with BCI systems can be highlighted, encouraging research groups to better coordinate in order to make valuable contributions to the field.

Rheumatoid arthritis (RA) is an autoimmune disorder that typically affects people between 23 and 60 years old causing chronic synovial inflammation, symmetrical polyarthritis, destruction of large and small joints, and chronic disability. Clinical diagnosis of RA is stablished by current ACR-EULAR criteria, and it is crucial for starting conventional therapy in order to minimize damage progression. The 2010 ACR-EULAR criteria include the presence of swollen joints, elevated levels of rheumatoid factor or anti-citrullinated protein antibodies (ACPA), elevated acute phase reactant, and duration of symptoms. In this paper, a computer-aided system for helping in the RA diagnosis, based on quantitative and easy-to-acquire variables, is presented. The participants in this study were all female, grouped into two classes: class I, patients diagnosed with RA (n = 100), and class II corresponding to controls without RA (n = 100). The novel approach is constituted by the acquisition of thermal and RGB images, recording their hand grip strength or gripping force. The weight, height, and age were also obtained from all participants. The color layout descriptors (CLD) were obtained from each image for having a compact representation. After, a wrapper forward selection method in a range of classification algorithms included in WEKA was performed. In the feature selection process, variables such as hand images, grip force, and age were found relevant, whereas weight and height did not provide important information to the classification. Our system obtains an AUC ROC curve greater than 0.94 for both thermal and RGB images using the RandomForest classifier. Thirty-eight subjects were considered for an external test in order to evaluate and validate the model implementation. In this test, an accuracy of 94.7% was obtained using RGB images; the confusion matrix revealed our system provides a correct diagnosis for all participants and failed in only two of them (5.3%). Graphical abstract.

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.

Motor imagery-based brain–computer interfaces decode users’ intentions from the electroencephalogram; however, poor spatial resolution makes automatic recognition of these intentions a challenging task. New classification approaches with low computational costs and high classification performances need to be developed in order to increase the number of users benefitted by these systems. On the other hand, spiking neuron models, which are mathematical abstractions of real neurons, have shown good performances in several classification tasks, making these models suitable for motor imagery classification. In this work, two different encoding strategies for spiking neuron models, applied to the classification of motor imagery time–frequency features of stroke patients and healthy subjects, were evaluated. Classification performances and computational costs of spiking neuron models were compared against those of linear discriminant analysis, support vector machines and artificial neural networks. Results showed that a time-varying encoding strategy is more suitable for motor imagery classification, and its implementation computational cost is low. Therefore, a spiking neuron model with a time-varying encoding strategy could increase the number of potential users of brain–computer interfaces.

Transcranial magnetic stimulation can be used as a tool for assessment of corticospinal tract integrity by extracting features from elicited motor evoked potentials (MEPs). However, the selection of MEPs from recordings that present them, and the computation of amplitude and latency are usually performed manually. A methodology based on computing summed power calculated by means of the Discrete Fourier Transform in 10-ms windows was used for automatization of this process and compared to manual measurements. Results showed that MEP selection coincided in 69% of cases, and amplitude and latency had average differences of 7% between methods. Therefore, automatization of MEP computations is feasible using frequency-based features.

Dendrite ellipsoidal neurons are a novel and different alternative for classification tasks, giving competitive results compared with typical classification methods. Based on k-means++ algorithm, the network allows each dendrite to build a hyperellipsoidal in order to assign each incoming pattern \(x_{i}=(x_{1},x_{2},\ldots ,x_{n})^{T}\) to its respective C class. The main disadvantage of this training algorithm is the lack of accuracy in high dimensional datasets. In this research, we solved this problem by training the dendrite ellipsoidal neuron using stochastic gradient descent. Furthermore, electroencephalography data were acquired during two mental conditions (imaginary movements of the left and right hand) in order to test the new training algorithm. The proposed algorithm outperformed the accuracy acquired by a dendrite ellipsoidal neuron based on k-means++ obtaining 76.02% and 62.77%, respectively. Also, the algorithm was compared with multilayer perceptrons and support vector machines which are some of the most common classifiers used to detect motor-related information in brain signals. These achieved an accuracy of 72.38% and 65.81%, respectively.

Brain injury volume computation has clinical importance since stroke patient's recovery may be assessed with it. Tools currently employed in the clinical field can only compute injury volumes with qualitative and manual methods. The present work proposes a semiautomatic computational method for calculating these volumes. Users of the method are only required to select a single point comprising the injury from one of the slides of a series of medical images. This seed point is used by the proposed algorithm to compute the average and standard deviation of the gray scale magnitude of adjacent pixels which comprise the injury. The method was tested using magnetic resonance images and compared with the volume manually computed by experts using a medical image viewer software. The proposed method showed an absolute error of 7.8%, compared with the averaged volume computed by the experts. Therefore, it is possible to estimate volumes of brain injuries with the proposed semi-automatic algorithm. This method could help decrease the time invested by clinical personnel in computing brain lesions' volumes.

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.To assess whether there are statistically significant gender differences in electroencephalography recorded during hand movements.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.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.Electroencephalography parameters used for the diagnosis of neuromotor diseases, as well as for brain-computer interface calibration, must take gender into account.

Spiking neural networks (SNN) have been successfully applied in pattern classification problems. However, their performance for solving complex problems such as electroencephalography (EEG) classification has not been widely assessed. It is necessary to consider new approaches to select relevant information and for training SNN in order to improve their accuracy when applied to complex data classification. In this paper, we present a novel channel selection and classification method based on SNN trained with Particle Swarm Optimization (PSO) for the classification of EEG signals associated to motor imagery. The proposed method was able to correctly identify the most relevant channels for different motor imagery tasks. The SNN trained with PSO achieved good classification performances for a well-studied public database using a minimal number of EEG channels, showing advantages against other approaches, regarding both performance and system requirements.

Individuals with stroke and other central nervous damage, which may cause paresis, are unable to move their affected limb or the movements are inefficient and clumsy. Brain-computer interfaces coupled with robotic assistive technologies such as robotic hand orthosis have the potential to provide rehabilitation strategies that promote brain plasticity for these patients. This paper presents the design of a control signal based on EEG signal processed using common spatial patterns and linear discriminant analysis to identify hand motor imagery. The control signal is implemented on a robotic hand orthosis so that it activates when a healthy subject performs motor imagery of her/his right hand, simulating an online signal acquisition. The mechatronic orthosis performance was always as indicated by the control signal, and the systems online performance for detecting motor imagery was of nearly 80% of correct classification. The system may be improved by using other classification algorithms however results show that it is ready to be tested with motor impaired patients.

In this work, a structure for an active mechatronics orthosis of right hand is presented. The mechanism was designed to help to patients with neuromuscular injuries who cannot grasp heavy objects. The Integration Definition for Function Modeling provides activities, functional requirements and anthropomorphic parameters to define the materials and components. Currently, the first prototype allows holding objects up 500 g.

Most of the recent brain-computer interfaces (BCI) based on motor imagery are designed by taking into account a single user. A BCI system designed for multiple users without the need of extensive training sessions could be a viable solution for the system's implementation outside research centers. The present work explores the design of an independent-subject BCI. In order to accomplish this, Linear Discriminant Analysis classifiers were designed with the data of a sample of 30 healthy volunteers as a whole group and separated by gender. Three different methods were employed to compute power spectrum features from the volunteer's electroencephalographic recordings. The results show that it is possible to design an independent-subject BCI for the classification of right or left hand motor imagery with respect of a reference interval with classification accuracies above 70%. The female gender could benefit more from a subject-independent classifier, than the male gender.

This work presents a module that aims to facilitate the acquisition of motor imagery tasks in electroencephalography (EEG) research. The device components are: a microcontroller which sets the time intervals of the events of a Graz type paradigm, and sends markers to an EEG acquisition system; a software that presents the visual and auditory clues of the paradigm in a personal computer (PC) for the test subject to perform the motor imagery tasks; and an algorithm aimed to extract the EEG information related to the motor imagery tasks. In the module validation, a delay of 1 ± 0.5 ms between the time in which the microcontroller marks the event in the EEG amplifier and the time in which the event is showed to the subject in the computer's monitor was measured. Furthermore, an average difference of 167 μs was obtained between the time intervals theoretically set for every event and the time intervals obtained. The module was tested in the EEG acquisition of motor imagery tasks in a BCI research protocol involving thirty healthy subjects. The module was successful at presenting the paradigm in all the trials and in indentifying the events of each trial in the signals that were recorded.

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 (p0.05) 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.

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: Al arger 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.