Happy faces selectively increase the excitability of cortical neurons innervating frowning muscles of the mouth.

Although facial muscles are heavily involved in emotional expressions, there is still a lack of evidence about the role of face primary motor cortex (face M1) in the processing of facial recognition and expression. This work investigated the effects of the passive viewing of different facial expressions on face M1 and compared data with those obtained from the hand M1. Thirty healthy subjects were randomly assigned to two groups undergoing transcranial magnetic stimulation (TMS) of face or hand M1. In both groups, short-latency intracortical inhibition (SICI) and intracortical facilitation (ICF) were probed in the depressor anguli oris (DAO) and first dorsal interosseous (FDI) muscles 300 ms after presentation of a picture of a face that expressed happy, sad or neutral emotions. Statistical analysis of SICI showed a non-significant effect of muscle (F1,28 = 1.903, p = 0.179), but a significant effect of emotion (F2,56 = 6.860, p = 0.004) and a significant interaction between muscle and emotion (F2,56 = 5.072, p = 0.015). Post hoc analysis showed that there was a significant reduction of SICI in the DAO muscle after presentation of a face with a happy expression compared with a neutral face (p < 0.001). In the FDI, a significant difference was observed between neutral and sad expressions (p = 0.010) No clear differences in ICF were detected. The different responses of face and hand muscles to emotional stimuli may be due to their functional roles in emotional expression versus protection of the body.

Use of Electroencephalography Brain-Computer Interface Systems as a Rehabilitative Approach for Upper Limb Function After a Stroke: A Systematic Review.

BACKGROUND: Brain-computer interface (BCI) systems have been suggested as a promising tool for neurorehabilitation. However, to date, there is a lack of homogeneous findings. Furthermore, no systematic reviews have analyzed the degree of validation of these interventions for upper limb (UL) motor rehabilitation poststroke. OBJECTIVES: The study aims were to compile all available studies that assess an UL intervention based on an electroencephalography (EEG) BCI system in stroke; to analyze the methodological quality of the studies retrieved; and to determine the effects of these interventions on the improvement of motor abilities. TYPE: This was a systematic review. LITERATURE SURVEY: Searches were conducted in PubMed, PEDro, Embase, Cumulative Index to Nursing and Allied Health, Web of Science, and Cochrane Central Register of Controlled Trial from inception to September 30, 2015. METHODOLOGY: This systematic review compiles all available studies that assess UL intervention based on an EEG-BCI system in patients with stroke, analyzing their methodological quality using the Critical Review Form for Quantitative Studies, and determining the grade of recommendation of these interventions for improving motor abilities as established by the Oxford Centre for Evidence-based Medicine. The articles were selected according to the following criteria: studies evaluating an EEG-based BCI intervention; studies including patients with a stroke and hemiplegia, regardless of lesion origin or temporal evolution; interventions using an EEG-based BCI to restore functional abilities of the affected UL, regardless of the interface used or its combination with other therapies; and studies using validated tools to evaluate motor function. SYNTHESIS: After the literature search, 13 articles were included in this review: 4 studies were randomized controlled trials; 1 study was a controlled study; 4 studies were case series studies; and 4 studies were case reports. The methodological quality of the included papers ranged from 6 to 15, and the level of evidence varied from 1b to 5. The articles included in this review involved a total of 141 stroke patients. CONCLUSIONS: This systematic review suggests that BCI interventions may be a promising rehabilitation approach in subjects with stroke. LEVEL OF EVIDENCE: II.

Entrenamiento de las señales corticales a través de un sistema BMI-EEG, evolución e intervención. A propósito de un caso / Training cortical signals by means of a BMI-EEG system, its evolution and intervention. A case report

Introducción. En los últimos años están incorporándose nuevas tecnologías en el tratamiento fisioterapéutico de pacientes con ictus, como las interfaces cerebro-máquina -brain-machine interface (BMI)-, capaces de detectar la intención de movimiento analizando las señales corticales por medio de diferentes técnicas, como la electroencefalografía (EEG). Estas señales se traducen en comandos con el fin de realizar una función. Caso clínico. Varón de 40 años con ictus de dos meses de evolución, en el cual se empleó un dispositivo BMI-EEG. La intención de movimiento del sujeto se analizó calculando la desincronización relacionada con el evento. La función motora del miembro superior fue evaluada con la escala de Fügl-Meyer, y el nivel de satisfacción del paciente, mediante el cuestionario QUEST 2.0. La intervención se llevó a cabo sin dificultad siendo el fisioterapeuta la interfaz. Conclusiones. Los sistemas BMI-EEG detectan cambios corticales en un sujeto con ictus subagudo. Estos cambios son coherentes con los cambios observados en escalas clínicas (AU)

Introduction. In the last years, new technologies such as the brain-machine interfaces (BMI) have been incorporated in the rehabilitation process of subjects with stroke. These systems are able to detect motion intention, analyzing the cortical signals using different techniques such as the electroencephalography (EEG). This information could guide different interfaces such as robotic devices, electrical stimulation or virtual reality. Case report. A 40 years-old man with stroke with two months from the injury participated in this study. We used a BMI based on EEG. The subject’s motion intention was analyzed calculating the event-related desynchronization. The upper limb motor function was evaluated with the Fügl-Meyer Assessment and the participant’s satisfaction was evaluated using the QUEST 2.0. The intervention using a physical therapist as an interface was carried out without difficulty. Conclusions. The BMI systems detect cortical changes in a subacute stroke subject. These changes are coherent with the evolution observed using the Fügl-Meyer Assessment (AU)