RESUMEN

Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imagery classification based on electroencephalogram (EEG) signals for the brain-computer interface (BCI), several methods have been proposed to extract features efficiently, mainly based on common spatial patterns, filter banks, and deep learning. However, most methods use only one dataset at a time, which may not be sufficient for dealing with a multi-source retrieving problem in certain scenarios. From this perspective, this paper proposes an original approach for feature extraction through multiple datasets based on IVA to improve the classification of EEG-based motor imagery movements. The IVA components were used as features to classify imagined movements using consolidated classifiers (support vector machines and K-nearest neighbors) and deep classifiers (EEGNet and EEGInception). The results show an interesting performance concerning the clustering of MI-based BCI patients, and the proposed method reached an average accuracy of 86.7%.

Asunto(s)

RESUMEN

Objective.Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals.Approach.A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN).Main results.A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN.Significance.Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

Asunto(s)

RESUMEN

The aim of this study was to evaluate mirror visual feedback (MVF) as a training tool for brain-computer interface (BCI) users. This is because approximately 20-30% of subjects require more training to operate a BCI system using motor imagery. Electroencephalograms (EEGs) were recorded from 18 healthy subjects, using event-related desynchronization (ERD) to observe the responses during the movement or movement intention of the hand for the conditions of control, imagination, and the MVF with the mirror box. We constituted two groups: group 1: control, imagination, and MVF; group 2: control, MVF, and imagination. There were significant differences in imagination conditions between groups using MVF before or after imagination (right-hand, P = 0.0403; left-hand, P = 0.00939). The illusion of movement through MVF is not possible in all subjects, but even in those cases, we found an increase in imagination when the subject used the MVF previously. The increase in the r2s of imagination in the right and left hands suggests cross-learning. The increase in motor imagery recorded with EEG after MVF suggests that the mirror box made it easier to imagine movements. Our results provide evidence that the MVF could be used as a training tool to improve motor imagery.NEW & NOTEWORTHY The increase in motor imagery recorded with EEG after MVF (mirror visual feedback) suggests that the mirror box made it easier to imagine movements. Our results demonstrate that MVF could be used as a training tool to improve motor imagery.

Asunto(s)

RESUMEN

Objective.This work proposes a method for two calibration schemes based on sensory feedback to extract reliable motor imagery (MI) features, and provide classification outputs more correlated to the user's intention.Method.After filtering the raw electroencephalogram (EEG), a two-step method for spatial feature extraction by using the Riemannian covariance matrices (RCM) method and common spatial patterns is proposed here. It uses EEG data from trials providing feedback, in an intermediate step composed of bothkth nearest neighbors and probability analyses, to find periods of time in which the user probably performed well the MI task without feedback. These periods are then used to extract features with better separability, and train a classifier for MI recognition. For evaluation, an in-house dataset with eight healthy volunteers and two post-stroke patients that performed lower-limb MI, and consequently received passive movements as feedback was used. Other popular public EEG datasets (such as BCI Competition IV dataset IIb, among others) from healthy subjects that executed upper-and lower-limbs MI tasks under continuous visual sensory feedback were further used.Results.The proposed system based on the Riemannian geometry method in two-steps (RCM-RCM) outperformed significantly baseline methods, reaching average accuracy up to 82.29%. These findings show that EEG data on periods providing passive movement can be used to contribute greatly during MI feature extraction.Significance.Unconscious brain responses elicited over the sensorimotor areas may be avoided or greatly reduced by applying our approach in MI-based brain-computer interfaces (BCIs). Therefore, BCI's outputs more correlated to the user's intention can be obtained.

Asunto(s)

RESUMEN

RESUMO. Esta pesquisa tem por objetivo investigar o uso do Grupo Multifamiliar Musicoterapêutico (GMM) junto a famílias socialmente vulneráveis. Trata-se de uma pesquisa-intervenção realizada em uma região administrativa do Distrito Federal. Participaram do estudo 30 famílias inscritas no Cadastro Único do governo federal e atendidas no Centro de Referência em Assistência Social. Os instrumentos de coleta de dados foram as visitas domiciliares, as entrevistas semiestruturadas e os registros dos encontros e das supervisões. O GMM foi realizado em seis encontros, com duração de 03 horas cada, com periodicidade quinzenal, intercalados com as supervisões da equipe, formada por 15 profissionais das áreas de psicologia, pedagogia, assistência social e musicoterapia. Após a análise foram identificados dois temas: 1) música, afetos e reminiscências; 2) música e sonhos. Percebeu-se que as experiências musicais auxiliaram as famílias na conscientização das formas violentas de comunicação e na transformação por meio de expressões afetuosas mediadas pela música e seu potencial de evocar memórias e sonhos. A capacidade imaginativa das famílias foi uma estratégia de enfrentamento às adversidades e se constituiu como ponte entre o real e o imaginário, nutrindo a esperança de uma vida melhor. Destaca-se o valor da música que, com rapidez e emocionalidade, acessa e comunica com o tal público, por favorecer intervenções musicoterapêuticas comunitárias.

RESUMEN. Este trabajo tiene como objetivo investigar el uso del Grupo Musicoterapéutico Multifamiliar (GMM) con familias socialmente vulnerables. Se trata de una intervención-investigación realizada en una Región Administrativa del Distrito Federal. Participaron del estudio 30 familias que se encuentran inscritas en el Registro Único del Gobierno Federal y que son atendidas en el Centro de Referencia de Asistencia Social. Los instrumentos de recolección de datos fueron: visitas domiciliarias, entrevistas semiestructuradas y registros de reuniones y supervisiones. El GMM fue realizado en seis encuentros, con una duración de tres horas cada uno. Los encuentros se realizaron cada dos semanas, intercalados con la supervisión del equipo, formado por 15 profesionales en las áreas de psicología, pedagogía, asistencia social y musicoterapia. Después del análisis temático, se identificaron dos temas: 1) música, afectos y reminiscencias; 2) y música y sueños. Se observó que las experiencias musicales ayudaron a las familias en la concientización de las formas violentas de comunicación y en la transformación por medio de expresiones afectuosas mediadas por la música y su potencial para evocar recuerdos y sueños. La capacidad imaginativa de las familias fue una estrategia para enfrentar las adversidades y se constituye como un puente entre lo real y lo imaginario, alimentando la esperanza de una vida mejor. Se destaca el valor de la música que, con rapidez y emotividad, accede en y se comunica con esa población, favoreciendo las intervenciones de musicoterapia comunitaria.

ABSTRACT: This research aims to investigate the use of Multi-family Music Therapy Group (MMG) with socially vulnerable families. This is an intervention research carried out in an Administrative Region in the Federal District of Brazil. The study included 30 families enrolled in the Federal Government's Single Registry and assisted at the Social Assistance Reference Center. The data collection instruments were: home visits, semi-structured interviews, and records of meetings and supervisions. The MMG was carried out in six meetings, lasting three hours each, every two weeks, interspersed with the supervision of the team, which was formed by 15 professionals from the fields of psychology, pedagogy, social assistance, and music therapy. After the thematic analysis, two themes were identified: 1) music, affections, and reminiscences; and 2) music and dreams. It was noticed that the musical experiences helped the families in the awareness of violent forms of communication and in the transformation through expressions of affection mediated by music and its potential to evoke memories and dreams. The families imaginative capacity was a strategy to face adversities and constitutes a bridge between the real and the imaginary, nurturing the hope of a better life. The value of music is highlighted, which quickly and emotionally accesses and communicates with that audience, favoring community music therapy interventions.

Asunto(s)

RESUMEN

AIM: To evaluate whether children with cerebral palsy (CP) are able to engage in a motor imagery task. Possible associations between motor imagery and functional performance, working memory, age, and intelligence were also investigated. METHOD: This is a case-control study that assessed 57 children (25 females, 32 males) with unilateral CP, aged 6 to 14 years (mean age: 10y 4mo; SD 2y 8mo) and 175 typically developing (control) children, aged 6 to 13 years (87 females, 88 males; mean age: 9y 4mo; SD 1y 11mo). The hand laterality judgment task was used to measure motor imagery ability. Reaction time, accuracy, and the effect of the biomechanical constraints were assessed in this task. RESULTS: Performance in both groups followed the biomechanical constraints of the task, that is, longer reaction times to recognize stimuli rotated laterally when compared to medial stimuli. Reaction time means did not differ significantly between groups (p>0.05). Significant differences between the unilateral CP and control groups were observed for accuracy (p<0.05). Functional performance and working memory were correlates of motor imagery tasks. INTERPRETATION: Results suggest that children with unilateral CP can engage in motor imagery; however, they commit more errors than typically developing controls. In addition, their performance in tasks of motor imagery is influenced by functional performance and working memory.

Asunto(s)

RESUMEN

INTRODUCTION: Deep brain stimulation (DBS) is a widely accepted therapy for Parkinson's disease. While outcome predictors such as levodopa-response are well established, there remains a need for objective and unbiased predictors in clinical practice. We performed an exploratory study to examine whether cortical thickness, derived from preoperative MRI, correlates with postoperative outcome. METHODS: Using freesurfer, we retrospectively measured cortical thickness on the preoperative MRI of 38 patients who underwent bilateral STN-DBS for PD during a 4-year period. The Unified Parkinson Disease Rating motor (UPDRS III) and experiences of daily living subscales (UPDRS II) were collected at baseline and six months after surgery. As an initial analysis, a series of partial correlations was conducted to evaluate the association between postoperative outcome scores and average cortical thickness from predefined regions of interest, adjusting for candidate confounders, without correcting for multiple comparisons. A confirmatory vertex-wise analysis was performed using a cluster-wise correction for multiple comparisons. RESULTS: Based on the ROI analysis, the strongest correlation with motor outcome was found to be with the left lateral-occipital cortex. Patients with greater cortical thickness in this area presented with greater improvements in motor scores. This relationship was also supported by the vertex-wise analysis. Greater cortical thickness in frontal and temporal regions may be correlated with greater post-operative improvements in UPDRS II, but this was not confirmed in the vertex-wise analysis. CONCLUSIONS: Our data indicate that greater cortical thickness in visuo-motor areas is correlated with motor outcomes after DBS for PD. Further prospective investigations are needed to confirm our findings and better-investigate potential image biomarkers.

Asunto(s)

RESUMEN

In this paper, we evaluate a semiautonomous brain-computer interface (BCI) for manipulation tasks. In such a system, the user controls a robotic arm through motor imagery commands. In traditional process-control BCI systems, the user has to provide those commands continuously in order to manipulate the effector of the robot step-by-step, which results in a tiresome process for simple tasks such as pick and replace an item from a surface. Here, we take a semiautonomous approach based on a conformal geometric algebra model that solves the inverse kinematics of the robot on the fly, and then the user only has to decide on the start of the movement and the final position of the effector (goal-selection approach). Under these conditions, we implemented pick-and-place tasks with a disk as an item and two target areas placed on the table at arbitrary positions. An artificial vision (AV) algorithm was used to obtain the positions of the items expressed in the robot frame through images captured with a webcam. Then, the AV algorithm is integrated into the inverse kinematics model to perform the manipulation tasks. As proof-of-concept, different users were trained to control the pick-and-place tasks through the process-control and semiautonomous goal-selection approaches so that the performance of both schemes could be compared. Our results show the superiority in performance of the semiautonomous approach as well as evidence of less mental fatigue with it.

Asunto(s)

RESUMEN

This work presents a classification performance comparison between different frameworks for functional connectivity evaluation and complex network feature extraction aiming to distinguish motor imagery classes in electroencephalography (EEG)-based brain-computer interfaces (BCIs). The analysis was performed in two online datasets: (1) a classical benchmark-the BCI competition IV dataset 2a-allowing a comparison with a representative set of strategies previously employed in this BCI paradigm and (2) a statistically representative dataset for signal processing technique comparisons over 52 subjects. Besides exploring three classical similarity measures-Pearson correlation, Spearman correlation, and mean phase coherence-this work also proposes a recurrence-based alternative for estimating EEG brain functional connectivity, which takes into account the recurrence density between pairwise electrodes over a time window. These strategies were followed by graph feature evaluation considering clustering coefficient, degree, betweenness centrality, and eigenvector centrality. The features were selected by Fisher's discriminating ratio and classification was performed by a least squares classifier in agreement with classical and online BCI processing strategies. The results revealed that the recurrence-based approach for functional connectivity evaluation was significantly better than the other frameworks, which is probably associated with the use of higher order statistics underlying the electrode joint probability estimation and a higher capability of capturing nonlinear inter-relations. There were no significant differences in performance among the evaluated graph features, but the eigenvector centrality was the best feature regarding processing time. Finally, the best ranked graph-based attributes were found in classical EEG motor cortex positions for the subjects with best performances, relating functional organization and motor activity. Graphical Abstract Evaluating functional connectivity based on Space-Time Recurrence Counting for motor imagery classification in brain-computer interfaces. Recurrences are evaluated between electrodes over a time window, and, after a density threshold, the electrodes adjacency matrix is stablish, leading to a graph. Graph-based topological measures are used for motor imagery classification.

Asunto(s)

RESUMEN

Recent findings have been challenging current understanding of how fast the human brain change its structural and functional connections in response to training. One powerful way to deepen the inner workings of human brain plasticity is using neurofeedback (NFB) by fMRI, a technique that allows self-induced brain plasticity by means of modulating brain activity in real time. In the present randomized, double-blind and sham-controlled study, we use NFB to train healthy individuals to reinforce brain patterns related to motor execution while performing a motor imagery task, with no overt movement. After 1 h of NFB training, participants displayed increased fractional anisotropy (FA) in the sensorimotor segment of corpus callosum and increased functional connectivity of the sensorimotor resting state network. Increased functional connectivity was also observed in the default mode network. These results were not observed in the control group, which was trained with sham feedback. To our knowledge, this is the first demonstration of white matter FA changes following a very short training schedule (<1 h). Our results suggest that NFB by fMRI can be an interesting tool to explore dynamic aspects of brain plasticity and open new venues for investigating brain plasticity in healthy individuals and in neurological conditions.

Asunto(s)

RESUMEN

OBJECTIVE: The aim of this study is to propose a recognition system of pedaling motor imagery for lower-limb rehabilitation, which uses unsupervised methods to improve the feature extraction, and consequently the class discrimination of EEG patterns. APPROACH: After applying a spectrogram based on short-time Fourier transform (SSTFT), both sparseness constraints and total power are used on the time-frequency representation to automatically locate the subject-specific bands that pack the highest power during pedaling motor imagery. The output frequency bands are employed in the recognition system to automatically adjust the cut-off frequency of a low-pass filter (Butterworth, 2nd order). Riemannian geometry is also used to extract spatial features, which are further analyzed through a fast version of neighborhood component analysis to increase the class separability. MAIN RESULTS: For ten healthy subjects, our recognition system based on subject-specific bands achieved mean accuracy of [Formula: see text] and mean Kappa of [Formula: see text]. SIGNIFICANCE: Our approach can be used to obtain a low-cost robotic rehabilitation system based on motorized pedal, as pedaling exercises have shown great potential for improving the muscular performance of post-stroke survivors.

Asunto(s)

RESUMEN

OBJECTIVE: Motor imagery brain-computer interfaces (MI-BCIs) based on electroencephalography (EEG), a promising technology to provide assistance and support rehabilitation of neurological patients with sensorimotor impairments, require a reliable and adaptable subject-specific model to efficiently decode motor intention. The most popular EEG feature extraction algorithm for MI-BCIs is the common spatial patterns (CSP) method, but its performance strongly depends on the predefined frequency band and time segment length for analyzing the EEG signal. APPROACH: In this work, a novel method for efficiently decoding motor intention for EEG-based BCIs performing multiple frequency band analysis in multiple EEG segments is presented. This decoding algorithm uses raw multichannel EEG data which are decomposed into specific [Formula: see text] temporal and [Formula: see text] frequency bands. Features are extracted at each [Formula: see text]-[Formula: see text] band by using CSP. Feature selection and classification are simultaneously performed by means of a fast procedure, based on elastic-net regression, which allows for the inclusion of a priori discriminative information into the model. The effectiveness of the proposed method is tested off-line on two public EEG-based MI-BCI datasets and on a self-acquired dataset in two configurations: multiple temporal windows and single temporal window. MAIN RESULTS: The experimental results show that the proposed multiple time-frequency band method yields overall accuracy improvements of up to [Formula: see text] (average accuracy of 84.8%) as compared to the best current state-of-the-art methods based on filter bank analysis and CSP for MI detection. Also, classification variability is reduced, making the proposed method more robust to intra-subject EEG fluctuations. SIGNIFICANCE: This paper presents a novel approach for improving motor intention detection by automatically selecting subject-specific spatio-temporal-spectral features, especially when MI has to be detected against rest condition. This technique contributes to the further advancement and application of EEG-based MI-BCIs for assistance and neurorehabilitation therapy.

Asunto(s)

RESUMEN

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.

Asunto(s)

RESUMEN

This study examined the factor structure of the Brazilian version of the Autism-Spectrum Quotient. This is a self-report questionnaire for continuous and quantitative assessment of autistic spectrum traits in adults. Confirmatory factor analysis was performed on the five-factor model (social skill, attention switching, attention to detail, communication and imagination) proposed by the original authors, support not being found for this model in our sample. An exploratory factor analysis was then performed that resulted in an alternative three-factor model (social skills, details/patterns and imagination). Confirmatory factor analysis of the latter model revealed adequate psychometric indexes. The Brazilian version of the AQ was shown to be an adequate instrument for the evaluation of signs compatible with the autism spectrum in adults.

Asunto(s)

RESUMEN

Cross-cultural comparisons of the prevalence of invisible/imaginary companions are difficult due to the use of various methods of data gathering and the lack of sampling in developing countries. The present study took place among 443 children (3-8-year-olds) in four different countries (Kenya, Malawi, Nepal and the Dominican Republic) employing the same interview method. Among all the children 21% affirmed that they had invisible/imaginary companions at the time of the interview. But the rates between countries varied significantly from a low of 5% in Nepal to a high of 34% in the Dominican Republic. The results suggest that the potential for the phenomenon transcends cultural particularity even as culture plays an important role for supporting or discouraging invisible/imaginary companions.

Asunto(s)

RESUMEN

Imagery is a widely spread technique in the sport sciences that entails the mental rehearsal of a given situation to improve an athlete's learning, performance and motivation. Two modalities of imagery are reported to tap into distinct brain structures, but sharing common components: kinesthetic and visual imagery. This study aimed to investigate the neural basis of those types of imagery with Activation Likelihood Estimation algorithm to perform a meta - analysis. A systematic search was used to retrieve only experimental studies with athletes or sportspersons. Altogether, nine studies were selected and an ALE meta - analysis was performed. Results indicated significant activation of the premotor, somatosensory cortex, supplementary motor areas, inferior and superior parietal lobule, caudate, cingulate and cerebellum in both imagery tasks. It was concluded that visual and kinesthetic imagery share similar neural networks which suggests that combined interventions are beneficial to athletes whereas separate use of those two modalities of imagery may seem less efficient from a neuropsychological approach.

Asunto(s)

RESUMEN

El entrenamiento con imaginación motora es una herramienta muy utilizada para mejorar la ejecución de técnicas deportivas. La teoría del procesamiento predictivo aplicada a la cognición ofrece una buena alternativa de explicación para este fenómeno, mostrando cómo fluye la información en el sistema motor mientras se ejecuta una acción y mientras se la imagina. Sin embargo, una errada taxonomía de los tipos de imaginación motora podría sentar las bases para construir un modelo de procesamiento predictivo que no explique ciertas peculiaridades que se dan en el entrenamiento de técnicas deportivas. En este sentido, propongo una corrección de la taxonomía estandarizada de modo que permita al modelo abarcar esas peculiaridades.

The training with motor imagery is a very used tool to improve the execution of sports techniques. The theory of predictive processing applied to cognition provides a good alternative explanation for this phenomenon, showing how information flows in the motor system while an action is executed and while it ́s imagined. However, a wrong taxonomy of types of motor imagery could lay the groundwork for constructing a predictive processing model that does not explain certain peculiarities that occur in the training of sports techniques. In this sense, I propose a correction of the standardized taxonomy on types of motor imagination, so as to allow the model to encompass these peculiarities.

Asunto(s)

RESUMEN

When attention is oriented toward inner thoughts, as spontaneously occurs during mind wandering, the processing of external information is attenuated. However, the potential effects of thought's content regarding sensory attenuation are still unknown. The present study aims to assess if the representational format of thoughts, such as visual imagery or inner speech, might differentially affect the sensory processing of external stimuli. We recorded the brain activity of 20 participants (12 women) while they were exposed to a probe visual stimulus in three different conditions: executing a task on the visual probe (externally oriented attention), and two conditions involving inward-turned attention i.e. generating inner speech and performing visual imagery. Event-related potentials results showed that the P1 amplitude, related with sensory response, was significantly attenuated during both task involving inward attention compared with external task. When both representational formats were compared, the visual imagery condition showed stronger attenuation in sensory processing than inner speech condition. Alpha power in visual areas was measured as an index of cortical inhibition. Larger alpha amplitude was found when participants engaged in an internal thought contrasted with the external task, with visual imagery showing even more alpha power than inner speech condition. Our results show, for the first time to our knowledge, that visual attentional processing to external stimuli during self-generated thoughts is differentially affected by the representational format of the ongoing train of thoughts.

Asunto(s)

RESUMEN

This experiment was designed to evaluate the effects of pure motor imagery training (MIT) and its combination with physical practice on learning an aiming task with the more affected arm in adolescents suffering from cerebral palsy. Effect of MIT was evaluated as a function of side of hemiparesis. The experiment was accomplished by 11- to 16-year-old participants (M = 13.58 years), who suffered left (n = 16) or right (n = 15) mild hemiparesis. They were exposed to pure MIT (day 1) followed by physical practice (day 2) on an aiming task demanding movement accuracy and speed. Posttraining movement kinematics of the group receiving MIT were compared with movement kinematics of the control group after receiving recreational activities (day 1) and physical practice (day 2). Kinematic analysis showed that MIT led to decreased movement time and straighter hand displacements to the target. Performance achievements from MIT were increased with further physical practice, leading to enhanced effects on motor learning. Retention evaluation indicated that performance improvement from pure MIT and its combination with physical practice were stable over time. Performance achievements were equivalent between adolescents with either right or left hemiparesis, suggesting similar capacity between these groups to achieve performance improvement from pure imagery training and from its association with physical practice. Our results suggest that motor imagery training is a procedure potentially useful to increase motor learning achievements in individuals suffering from cerebral palsy.

Asunto(s)

RESUMEN

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.

Asunto(s)