Mapping entrained brain oscillations during transcranial alternating current stimulation (tACS).

Transcranial alternating current stimulation (tACS), a non-invasive and well-tolerated form of electric brain stimulation, can influence perception, memory, as well as motor and cognitive function. While the exact underlying neurophysiological mechanisms are unknown, the effects of tACS are mainly attributed to frequency-specific entrainment of endogenous brain oscillations in brain areas close to the stimulation electrodes, and modulation of spike timing dependent plasticity reflected in gamma band oscillatory responses. tACS-related electromagnetic stimulator artifacts, however, impede investigation of these neurophysiological mechanisms. Here we introduce a novel approach combining amplitude-modulated tACS during whole-head magnetoencephalography (MEG) allowing for artifact-free source reconstruction and precise mapping of entrained brain oscillations underneath the stimulator electrodes. Using this approach, we show that reliable reconstruction of neuromagnetic low- and high-frequency oscillations including high gamma band activity in stimulated cortical areas is feasible opening a new window to unveil the mechanisms underlying the effects of stimulation protocols that entrain brain oscillatory activity.

Simultaneous transcranial direct current stimulation (tDCS) and whole-head magnetoencephalography (MEG): assessing the impact of tDCS on slow cortical magnetic fields.

Transcranial direct current stimulation (tDCS) can influence cognitive, affective or motor brain functions. Whereas previous imaging studies demonstrated widespread tDCS effects on brain metabolism, direct impact of tDCS on electric or magnetic source activity in task-related brain areas could not be confirmed due to the difficulty to record such activity simultaneously during tDCS. The aim of this proof-of-principal study was to demonstrate the feasibility of whole-head source localization and reconstruction of neuromagnetic brain activity during tDCS and to confirm the direct effect of tDCS on ongoing neuromagnetic activity in task-related brain areas. Here we show for the first time that tDCS has an immediate impact on slow cortical magnetic fields (SCF, 0-4Hz) of task-related areas that are identical with brain regions previously described in metabolic neuroimaging studies. 14 healthy volunteers performed a choice reaction time (RT) task while whole-head magnetoencephalography (MEG) was recorded. Task-related source-activity of SCFs was calculated using synthetic aperture magnetometry (SAM) in absence of stimulation and while anodal, cathodal or sham tDCS was delivered over the right primary motor cortex (M1). Source reconstruction revealed task-related SCF modulations in brain regions that precisely matched prior metabolic neuroimaging studies. Anodal and cathodal tDCS had a polarity-dependent impact on RT and SCF in primary sensorimotor and medial centro-parietal cortices. Combining tDCS and whole-head MEG is a powerful approach to investigate the direct effects of transcranial electric currents on ongoing neuromagnetic source activity, brain function and behavior.

Brain-machine interface in chronic stroke rehabilitation: a controlled study.

OBJECTIVE: Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double-blind sham-controlled design proof of concept study. METHODS: Thirty-two chronic stroke patients with severe hand weakness were randomly assigned to 2 matched groups and participated in 17.8 ± 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms with contingent online movements of hand and arm orthoses (experimental group, n = 16). In the control group (sham group, n = 16), movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo-expectancy effects, and functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent activity were assessed before and after intervention. RESULTS: A significant group × time interaction in upper limb (combined hand and modified arm) Fugl-Meyer assessment (cFMA) motor scores was found. cFMA scores improved more in the experimental than in the control group, presenting a significant improvement of cFMA scores (3.41 ± 0.563-point difference, p = 0.018) reflecting a clinically meaningful change from no activity to some in paretic muscles. cFMA improvements in the experimental group correlated with changes in fMRI laterality index and with paretic hand electromyography activity. Placebo-expectancy scores were comparable for both groups. INTERPRETATION: The addition of BMI training to behaviorally oriented physiotherapy can be used to induce functional improvements in motor function in chronic stroke patients without residual finger movements and may open a new door in stroke neurorehabilitation.

Functional synergy recruitment index as a reliable biomarker of motor function and recovery in chronic stroke patients.

Objective. Stroke affects the expression of muscle synergies underlying motor control, most notably in patients with poorer motor function. The majority of studies on muscle synergies have conventionally approached this analysis by assuming alterations in the inner structures of synergies after stroke. Although different synergy-based features based on this assumption have to some extent described pathological mechanisms in post-stroke neuromuscular control, a biomarker that reliably reflects motor function and recovery is still missing.Approach. Based on the theory of muscle synergies, we alternatively hypothesize that functional synergy structures are physically preserved and measure the temporal correlation between the recruitment profiles of healthy modules by paretic and healthy muscles, a feature hereafter reported as the FSRI. We measured clinical scores and extracted the muscle synergies of both ULs of 18 chronic stroke survivors from the electromyographic activity of 8 muscles during bilateral movements before and after 4 weeks of non-invasive BMI controlled robot therapy and physiotherapy. We computed the FSRI as well as features quantifying inter-limb structural differences and evaluated the correlation of these synergy-based measures with clinical scores.Main results. Correlation analysis revealed weak relationships between conventional features describing inter-limb synergy structural differences and motor function. In contrast, FSRI values during specific or combined movement data significantly correlated with UL motor function and recovery scores. Additionally, we observed that BMI-based training with contingent positive proprioceptive feedback led to improved FSRI values during the specific trained finger extension movement.Significance. We demonstrated that FSRI can be used as a reliable physiological biomarker of motor function and recovery in stroke, which can be targeted via BMI-based proprioceptive therapies and adjuvant physiotherapy to boost effective rehabilitation.

Brain-Machine Interface in Chronic Stroke: Randomized Trial Long-Term Follow-up.

BACKGROUND: Brain-machine interfaces (BMIs) have been recently proposed as a new tool to induce functional recovery in stroke patients. OBJECTIVE: Here we evaluated long-term effects of BMI training and physiotherapy in motor function of severely paralyzed chronic stroke patients 6 months after intervention. METHODS: A total of 30 chronic stroke patients with severe hand paresis from our previous study were invited, and 28 underwent follow-up assessments. BMI training included voluntary desynchronization of ipsilesional EEG-sensorimotor rhythms triggering paretic upper-limb movements via robotic orthoses (experimental group, n = 16) or random orthoses movements (sham group, n = 12). Both groups received identical physiotherapy following BMI sessions and a home-based training program after intervention. Upper-limb motor assessment scores, electromyography (EMG), and functional magnetic resonance imaging (fMRI) were assessed before (Pre), immediately after (Post1), and 6 months after intervention (Post2). RESULTS: The experimental group presented with upper-limb Fugl-Meyer assessment (cFMA) scores significantly higher in Post2 (13.44 ± 1.96) as compared with the Pre session (11.16 ± 1.73; P = .015) and no significant changes between Post1 and Post2 sessions. The Sham group showed no significant changes on cFMA scores. Ashworth scores and EMG activity in both groups increased from Post1 to Post2. Moreover, fMRI-BOLD laterality index showed no significant difference from Pre or Post1 to Post2 sessions. CONCLUSIONS: BMI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis and represents a promising strategy in severe stroke neurorehabilitation.

Is EMG a Viable Alternative to BCI for Detecting Movement Intention in Severe Stroke?

OBJECTIVE: In light of the shortcomings of current restorative brain-computer interfaces (BCI), this study investigated the possibility of using EMG to detect hand/wrist extension movement intention to trigger robot-assisted training in individuals without residual movements. METHODS: We compared movement intention detection using an EMG detector with a sensorimotor rhythm based EEG-BCI using only ipsilesional activity. This was carried out on data of 30 severely affected chronic stroke patients from a randomized control trial using an EEG-BCI for robot-assisted training. RESULTS: The results indicate the feasibility of using EMG to detect movement intention in this severely handicapped population; probability of detecting EMG when patients attempted to move was higher (p 0.001) than at rest. Interestingly, 22 out of 30 (or 73%) patients had sufficiently strong EMG in their finger/wrist extensors. Furthermore, in patients with detectable EMG, there was poor agreement between the EEG and EMG intent detectors, which indicates that these modalities may detect different processes. CONCLUSION: A substantial segment of severely affected stroke patients may benefit from EMG-based assisted therapy. When compared to EEG, a surface EMG interface requires less preparation time, which is easier to don/doff, and is more compact in size. SIGNIFICANCE: This study shows that a large proportion of severely affected stroke patients have residual EMG, which yields a direct and practical way to trigger robot-assisted training.

Decoding Sensorimotor Rhythms during Robotic-Assisted Treadmill Walking for Brain Computer Interface (BCI) Applications.

Locomotor malfunction represents a major problem in some neurological disorders like stroke and spinal cord injury. Robot-assisted walking devices have been used during rehabilitation of patients with these ailments for regaining and improving walking ability. Previous studies showed the advantage of brain-computer interface (BCI) based robot-assisted training combined with physical therapy in the rehabilitation of the upper limb after stroke. Therefore, stroke patients with walking disorders might also benefit from using BCI robot-assisted training protocols. In order to develop such BCI, it is necessary to evaluate the feasibility to decode walking intention from cortical patterns during robot-assisted gait training. Spectral patterns in the electroencephalogram (EEG) related to robot-assisted active and passive walking were investigated in 10 healthy volunteers (mean age 32.3±10.8, six female) and in three acute stroke patients (all male, mean age 46.7±16.9, Berg Balance Scale 20±12.8). A logistic regression classifier was used to distinguish walking from baseline in these spectral EEG patterns. Mean classification accuracies of 94.0±5.4% and 93.1±7.9%, respectively, were reached when active and passive walking were compared against baseline. The classification performance between passive and active walking was 83.4±7.4%. A classification accuracy of 89.9±5.7% was achieved in the stroke patients when comparing walking and baseline. Furthermore, in the healthy volunteers modulation of low gamma activity in central midline areas was found to be associated with the gait cycle phases, but not in the stroke patients. Our results demonstrate the feasibility of BCI-based robotic-assisted training devices for gait rehabilitation.

Decoding upper limb residual muscle activity in severe chronic stroke.

OBJECTIVE: Stroke is a leading cause of long-term motor disability. Stroke patients with severe hand weakness do not profit from rehabilitative treatments. Recently, brain-controlled robotics and sequential functional electrical stimulation allowed some improvement. However, for such therapies to succeed, it is required to decode patients' intentions for different arm movements. Here, we evaluated whether residual muscle activity could be used to predict movements from paralyzed joints in severely impaired chronic stroke patients. METHODS: Muscle activity was recorded with surface-electromyography (EMG) in 41 patients, with severe hand weakness (Fugl-Meyer Assessment [FMA] hand subscores of 2.93 ± 2.7), in order to decode their intention to perform six different motions of the affected arm, required for voluntary muscle activity and to control neuroprostheses. Decoding of paretic and nonparetic muscle activity was performed using a feed-forward neural network classifier. The contribution of each muscle to the intended movement was determined. RESULTS: Decoding of up to six arm movements was accurate (>65%) in more than 97% of nonparetic and 46% of paretic muscles. INTERPRETATION: These results demonstrate that some level of neuronal innervation to the paretic muscle remains preserved and can be used to implement neurorehabilitative treatments in 46% of patients with severe paralysis and extensive cortical and/or subcortical lesions. Such decoding may allow these patients for the first time after stroke to control different motions of arm prostheses through muscle-triggered rehabilitative treatments.

Cortex integrity relevance in muscle synergies in severe chronic stroke.

BACKGROUND: Recent experimental evidence has indicated that the motor system coordinates muscle activations through a linear combination of muscle synergies that are specified at the spinal or brainstem networks level. After stroke upper limb impairment is characterized by abnormal patterns of muscle activations or synergies. OBJECTIVE: This study aimed at characterizing the muscle synergies in severely affected chronic stroke patients. Furthermore, the influence of integrity of the sensorimotor cortex on synergy modularity and its relation with motor impairment was evaluated. METHODS: Surface electromyography from 33 severely impaired chronic stroke patients was recorded during 6 bilateral movements. Muscle synergies were extracted and synergy patterns were correlated with motor impairment scales. RESULTS: Muscle synergies extracted revealed different physiological patterns dependent on the preservation of the sensorimotor cortex. Patients without intact sensorimotor cortex showed a high preservation of muscle synergies. On the contrary, patients with intact sensorimotor cortex showed poorer muscle synergies preservation and an increase in new generated synergies. Furthermore, the preservation of muscle synergies correlated positively with hand functionality in patients with intact sensorimotor cortex and subcortical lesions only. CONCLUSION: Our results indicate that severely paralyzed chronic stroke patient with intact sensorimotor cortex might sculpt new synergy patterns as a response to maladaptive compensatory strategies.

INTERFAZ CEREBRO COMPUTADORA (ICC) BASADA EN EL POTENCIAL RELACIONADO CON EVENTOS P300: ANÁLISIS DEL EFECTO DE LA DIMENSIÓN DE LA MATRIZ DE ESTIMULACIÓN SOBRE SU DESEMPEÑO

Una interfaz cerebro computadora (ICC) es un dispositivo que ayuda a personas con deficiencias motoras severas, al permitir la realización de una comunicación externa a partir de la actividad eléctrica del cerebro sin la asistencia de los nervios periféricos o de la actividad muscular, prometiendo además una mejora en la calidad de vida de los pacientes. En este proyecto se utilizó un sistema ICC basado en el paradigma P300, desarrollado en la Universidad Nacional de Entre Ríos. El sistema cuenta con un sistema no invasivo de adquisición de electroencefalograma, un amplificador Grass, el software BCI2000 y el paquete de simulación robótica Marilou. Adicionalmente, el sistema permite evaluar la aplicación de dicha ICC en el control de una silla de ruedas autopropulsada e inteligente. La presentación de estímulos para la generación del P300 se llevó a cabo con matrices de íconos que codifican las instrucciones de comandos o direcciones para la silla de ruedas. En el presente trabajo se probaron dos matrices con diferentes dimensiones y distribuciones, la primera de 4x5 y la segunda de 4x3. Se analizaron los porcentajes de clasificación que éstas arrojaron con el método de regresión SWLDA, donde se concluyó que la matriz de 4x3 presentaba mayores porcentajes de clasificación que la matriz 4x5. Las implicaciones con respecto al control de la silla se vislumbran como mayor confort y exactitud en el sistema inteligente.

A brain computer interface BCI is a device that helps people with severs motor disabilities. It allows an external communication through the electrical activity of the brain without the assistance of the peripheral nerves or muscle activity. This project used a BCI system, based on P300 paradigm which was developed at Universidad Nacional de Entre Ríos. The system includes an EEG signal acquisition system that use external electrodes, a Grass amplifier, the BCI2000 software, and the Marilou robotic simulation tool. Additionally, the system allows the evaluation of the BCI application to control the movement of an intelligent and self-propelled wheelchair. The presentation of icons, which codified the instructions to command the wheelchair movements, was developed, in order to generate the stimulus for P300 generation. Two matrix with different size and distribution (4x5 and 4x3, row x column) were tested. We analyzed the percentage of classification obtained after the application of the regression method SWLDA, and we found that the major classification percentage was achieved with the 4x3 matrix. This study reveals that this process could be faster and more confortable for the user. And finally the subject decisions will have more correlation between the results of the system and his real desire.

Evaluación experimental y estadística de un prototipo de interfaz cerebro-computador (ICC) / Experimental and statistical evaluation of a brain-computer interface (BCI) prototype

En la actualidad, las Interfaces Cerebro-Computador (ICC) se diseñan con el fin de usarlas tanto en estudios experimentales como clínicos, y cuyos resultados permiten la creación de nuevas tecnologías asistidas para personas que se encuentran en situación de discapacidad motora. En el año 2008 se desarrolló un prototipo de una ICC en la Escuela de Ingeniería de Antioquia y la Universidad CES, la cual hace uso de los potenciales evocados cognitivos P300 mediante electroencefalografía (EEG). En este trabajo se propone un estudio experimental y estadístico para comparar un prototipo de ICC con un sistema comercial (USBamp), estudiando si existen diferencias significativas entre los dos sistemas. El estudio se concentra en pruebas destinadas a la caracterización de sistemas empleando como entrada, inicialmente, señales determinísticas con diferentes valores de frecuencia y amplitud, y cuya evaluación se hace a través del valor cuadrático medio, la densidad espectral de las señales, el tiempo de respuesta y el máximo pico ante un estímulo. En segunda instancia, se realizan pruebas análogas en señales de P300 evaluando la energía de la señal y el tiempo de latencia por canal. Se hace uso de elementos de inferencia estadística como la evaluación de hipótesis para dos medias suponiendo varianzas desconocidas iguales y prueba de medias para dos muestras pareadas. De las pruebas evaluadas se concluye que la ICC es apta en cuanto la adquisición de EEG y su procesamiento, pero se establecen planes de mejoramiento para algunos tratamientos que incluyen el diseño de nuevos circuitos para mejorar el ancho de banda.

Nowadays, brain-computer interfaces (BCI) are designed to use them in experimental and clinical studies, which results allow the creation of new assistive technologies for people with motor disabilities. In 2008, a prototype of a BCI was developed in the School of Engineering of Antioquia and University CES, which uses the cognitive P300 evoked potential recorded by electroencephalography (EEG). In this paper we propose an experimental and statistical design to compare our BCI prototype with a commercial device (USBamp), studying if they show significant differences or not. At first instance, this study is focused in some tests that characterize the systems using as input, deterministic signals with different values of frequency and amplitude, and which evaluation is made through mean square value, signals spectral density, response time and maximum peak during a stimulus. Secondly, we performed some analog tests in P300 signals evaluating signal energy and latency per channel. We use elements of statistical inference such as: the evaluation of a hypothesis for two means assuming unknown equal variances and equal means tests for two paired samples. According to the evidence, we concluded that our BCI is suitable to measure and process EEG signals but is necessary to establish some improvement for certain treatments such as: the design of new circuits to optimize band width.