Flickering exercise video produces mirror neuron system (MNS) activation and steady state visually evoked potentials (SSVEPs)

The action of observing can be used as an effective rehabilitation paradigm, because it activates the mirror neuron system. However, it is difficult to fully use this paradigm because it is difficult to get patients to engage in watching video clips of exercise. In this study, we proposed a steady state visually evoked potential (SSVEP) based paradigm that could be used in a Brain Computer Interface, and examined its feasibility by investigating whether flickering video could activate the mirror neuron system and evoke SSVEPs at the same time. Twenty subjects were recruited and asked to watch the flickering videos at a rate of 20 Hz of upper limb motion and visual white noise, while an EEG signal was recorded. The mu rhythm (8–13 Hz) suppression and the SSVEP (19–21 Hz) evocation were analyzed from recorded EEG. The results showed that SSVEPs, evoked by the flickering stimulus, was observed in both conditions on O1 and O2, but the mu rhythm suppression on C3 and C4 was observed only in the exercise video condition. These results could signify that the flickering video is applicable for the BCI rehabilitation game, activating the mirror neuron system at the same time.

Análisis de señales electroencefalográficas para la clasificación de habla imaginada / Electroencephalographic Signals Analysis for Imagined Speech Classification

El presente trabajo tiene como objetivo interpretar las señales de EEG registradas durante la pronunciación imaginada de palabras de un vocabulario reducido, sin emitir sonidos ni articular movimientos (habla imaginada o no pronunciada) con la intención de controlar un dispositivo. Específicamente, el vocabulario permitiría controlar el cursor de la computadora, y consta de las palabras del lenguaje español: "arriba", "abajo", "izquierda", "derecha", y "seleccionar". Para ello, se registraron las señales de EEG de 27 individuos utilizando un protocolo básico para saber a priori en qué segmentos de la señal la persona imagina la pronunciación de la palabra indicada. Posteriormente, se utiliza la transformada wavelet discreta (DWT) para extraer características de los segmentos que son usados para calcular la energía relativa wavelet (RWE) en cada una de los niveles en los que la señal es descompuesta, y se selecciona un subconjunto de valores RWE provenientes de los rangos de frecuencia menores a 32 Hz. Enseguida, éstas se concatenan en dos configuraciones distintas: 14 canales (completa) y 4 canales (los más cercanos a las áreas de Broca y Wernicke). Para ambas configuraciones se entrenan tres clasificadores: Naive Bayes (NB), Random Forest (RF) y Máquina de vectores de soporte (SVM). Los mejores porcentajes de exactitud se obtuvieron con RF cuyos promedios fueron 60.11% y 47.93% usando las configuraciones de 14 canales y 4 canales, respectivamente. A pesar de que los resultados aún son preliminares, éstos están arriba del 20%, es decir, arriba del azar para cinco clases. Con lo que se puede conjeturar que las señales de EEG podrían contener información que hace posible la clasificación de las pronunciaciones imaginadas de las palabras del vocabulario reducido.

This work aims to interpret the EEG signals associated with actions to imagine the pronunciation of words that belong to a reduced vocabulary without moving the articulatory muscles and without uttering any audible sound (imagined or unspoken speech). Specifically, the vocabulary reflects movements to control the cursor on the computer, and consists of the Spanish language words: "arriba", "abajo", "izquierda", "derecha", and "seleccionar". To do this, we have recorded EEG signals from 27 subjects using a basic protocol to know a priori in what segments of the signal a subject imagines the pronunciation of the indicated word. Subsequently, discrete wavelet transform (DWT) is used to extract features from the segments. These are used to compute relative wavelet energy (RWE) in each of the levels in that EEG signal is decomposed and, it is selected a RWE values subset with the frequencies smaller than 32 Hz. Then, these are concatenated in two different configurations: 14 channels (full) and 4 channels (the channels nearest to the brain areas of Wernicke and Broca). The following three classifiers were trained using both configurations: Naive Bayes (NB), Random Forest (RF) and support vector machines (SVM). The best accuracies were obtained by RF whose averages were 60.11% and 47.93% using both configurations, respectively. Even though, the results are still preliminary, these are above 20%, this means they are more accurate than chance for five classes. Based on them, we can conjecture that the EEG signals could contain information needed for the classification of the imagined pronunciations of the words belonging to a reduced vocabulary.

Multiple mental tasks classification based on nonlinear parameter of mean period using support vector machines / 西安交通大学学报·英文版

Mental task classification is one of the most important problems in Brain-computer interface. This paper studies the classification of five-class mental tasks. The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM (support vector machines). The averaged classification accuracy of 85. 6% over 7 subjects was achieved for 2-second EEG segments. And the results for EEG segments of 0. 5s and 5. 0s compared favorably to those of Garrett's. The results indicate that the parameter of mean period represents mental tasks well for classification. Furthermore, the method of mean period is less computationally demanding, which indicates its potential use for online BCI systems.

Multiple mental tasks classification based on nonlinear parameter of mean period using support vector machines / 药物分析学报

Mental task classification is one of the most important problems in Brain-computer interface. This paper studies the classification of five-class mental tasks. The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM (support vector machines). The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments. And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's. The results indicate that the parameter of mean period represents mental tasks well for classification. Furthermore, the method of mean period is less computationally demanding, which indicates its potential use for online BCI systems.

Research on control system of brain-computer interface based on the alpha waves in EEG / 医疗卫生装备

A system design of brain-computer interface based on the alpha waves in human electroencephalography(EEG) is presented in this paper.With the effects on the alpha wave amplitudes of human eye's open and close involved in,the selection control of four direction targets can be performed on a computer screen.The system speed and accuracy rate are investigated through the experiments involving 5 subjects.It is shown that the system is easy to operate and needs no complex learning and biofeedback training.The studying results provide a good technical foundation for the development of BCI control panel and the realization of the system integration.It has the potential application for clinical engineering and is valuable for further research.

Research advances and outlook on the brain-computer interface / 医疗卫生装备

Experimental methods and some key techniques of brain-computer interface(BCI)are introduced in this paper.The further discussion is mainly focused on the research of practical BCIs.