Actividad eléctrica cerebral de la atención en adolescentes policonsumidores por medio de un equipo de BCI (Brain Control Interface) / Atividade elétrica cerebral da atenção em adolescentes policonsumidores por meio de um equipamento de Brain Control Interface (BCI) / Brain electrical activity of attention in polydrug adolescents using an equipment BCI (Brain Control Interface)

Resumen El consumo de sustancias psicoactivas es un problema de salud pública que afecta cada vez más a la población adolescente. La presente investigación tuvo como objetivo registrar la actividad eléctrica cerebral (EEG) en tareas de atención (sostenida y selectiva) en un grupo de adolescentes policonsumidores. Se empleó un diseño ex post-facto retrospectivo con grupo cuasi control, en 46 adolescentes con edades entre los 12 los 17 años: 23 policonsumidores y 23 cuasi-controles. Para el registro de la actividad eléctrica cerebral se utilizó un equipo de BCI (Brain Control Interface) Emotiv EPOC research grade 14 Channel Mobile EEG y se aplicó el Programa virtual de entrenamiento cerebral Brain HQ con el módulo "enfoco mi atención" para la evaluación de la atención. Los resultados mostraron un incremento de ondas cerebrales beta-β (13-30 Hz), theta-θ (4-7 Hz) y delta-δ (3-4 Hz) en áreas frontales y prefrontales en los adolescentes policonsumidores en tareas de atención en comparación con el grupo cuasi-control. Se identificó una diferencia significativa con respecto al tiempo de respuesta entre los adolescentes consumidores de sustancias psicoactivas frente al grupo cuasi-control en ambos tipos de tareas atencionales.

Resumo O consumo de substâncias psicoativas é um problema de saúde pública que afeta cada vez mais a população adolescente. Esta pesquisa teve como objetivo registrar a atividade elétrica cerebral (EEG) em tarefas de atenção (sustentada e alternada) num grupo de adolescentes policonsumidores. Foi empregado um desenho ex post-facto retrospectivo com grupo quasecontrole, em en 46 adolescentes entre 12 e 17 anos de idade: 23 policonsumidores e 23 quase-controles. Para o registro da atividade elétrica cerebral, foi utilizado um equipamento de Brain Control Interface (BCI) Emotiv EPOC research grade 14 Channel Mobile EEG e foi aplicado o Programa Virtual de Treinamento Cerebral Brain HQ, com o módulo "foco minha atenção" para a avaliar a atenção. Os resultados mostraram um aumento de ondas cerebrais beta-β (13-30 Hz), theta-θ (4-7 Hz) e delta-δ (3-4 Hz) em áreas frontais e pré-frontais nos adolescentes policonsumidores em tarefas de atenção em comparação com o grupo quase-controle. Foi identificada uma diferença significativa a respeito do tempo de resposta entre os adolescentes consumidores de substâncias psicoativas ante o grupo quase-controle em ambos os tipos de tarefas de atenção.

Abstract The consumption of psychoactive substances is a public health problem that increasingly affects the adolescent population. This investigation had the objective of record the brain electrical activity (EEG) in attention tasks (sustained and selective) in a group of polyconsumers. Employment a retrospective ex post-facto design with a quasi-control group with 46 adolescents between 12-17 years old: 23 polyconsumers and 23 quasi-controls. For the recording of brain electrical activity, it was used a equipment BCI (Brain Control Interface) research grade 14 Channel Mobile EEG and applied the Brain Training Virtual Program "Brain HQ" module "focus my attention" to evaluate the attention. The results showed an increase in beta-β (1330 Hz), theta-θ (4-7 Hz) and delta-δ (3-4 Hz) brain waves in frontal and prefrontal areas in adolescent polyonsumers versus the quasi-control group in attention tasks. Likewise, identified a significant difference with respect to the response time between adolescents consuming psychoactive substances in relation to the quasi-control group in both types of attentional tasks.

Classification of Imaginary Motor Task from Electroencephalographic Signals: A Comparison of Feature Selection Methods and Classification Algorithms / Clasificación de una Motricidad Imaginaria por medio de Señales Electroencefalográficas: Una Comparación de Métodos de Selección de Características y Algoritmos de Clasificación

Abstract: In this work, a Brain Computer interface able to decode imagery motor task from EEG is presented. The method uses time-frequency representation of the brain signal recorded in different regions of the brain to extract important features. Principal Component Analysis and Sequential Forward Selection methods are compared in their ability to represent the feature set in a compact form, removing at the same time unnecessary information. Finally, two method based on machine learning are implemented for the task of classification. Results show that it is possible to decode the mental activity of the subjects with accuracy above 80%. Furthermore, visualization of the main components extracted from the brain signal allow for physiological insights on the activity that take place in the sensorimotor cortex during execution of imaginary movement of different parts of the body.

Resumen: En este trabajo es presentada una Interfaz Cerebro Computadora que tiene la capacidad de decodificar actividades motrices. El método utiliza representación en el dominio de la frecuencia y el tiempo de las señales del cerebro grabadas en distintas regiones de este mismo, con el fin de extraer características importantes. Los métodos: Análisis de Componentes Principales y Selección Secuencial, son comparados en términos de su capacidad para representar características de la señal de una forma compacta, removiendo de esta forma, información innecesaria. Finalmente, dos métodos basados en aprendizaje de máquinas fueron implementados para la clasificación de actividades motrices utilizando solo las señales cerebrales. Los resultados muestran que es posible decodificar la actividad mental en los sujetos con una precisión superior al 80%. Además, la visualización de las componentes principales extraídas de las señales del cerebro permite un analísis de la actividad que toma lugar en la corteza cerebral sensorimotora durante la ejecución de la imaginación de movimientos de distintas partes del cuerpo.

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.

Identificación y descripción de la intimidación escolar en instituciones educativas del municipio de Popayán / Identification and description of school bullying in institutions in the municipality of Popayan

Se identifica y describe el fenomeno de la intimidación escolar en una muestra de 1.300 estudiantes, 513 padres y 81 docentes de 5 instituciones educativas del municipio de Popayán, Colombia. Se utilizó metodología cuantitativa-descriptiva y análisis con SPSS en frecuencias relativas. Los resultados demuestran la existencia de intimidación física en el 44 % a través de golpes, patadas y quitar elementos escolares, e intimidación psicológica en el 70 % incluyendo gritar a un compañero, poner apodos, coaccionar, amenazar y excluir. En el 35.1 % los profesores consideran el problema como grave en su institución, y que es necesaria su reflexión en el 71.6 %, el 31.4 % de los padres consideran muy grave el problema.

he goal was to describe school bullying is identified and described in a sample of 1300 students, 513 parents and 81 teachers from five schools in Popayán, Colombia. We used quantitative methodology to describe and analyze in terms of relative frequencies with SPSS. Results show a 44% prevalence of school bullying, through punches, kicks, theft of school materials, and psychological intimidation in 70%, including yelling at peers, name-calling, coercion, threats and exclusion. 35.1% of the teachers and 31.4% of the parents regard the situation as significant, and 71.6% consider that a reflection on the subject is needed.

A self-organizing maps classifier structure for brain computer interfaces

AbstractIntroductionBrain Computer Interfaces provide an alternative communication path to severe paralyzed people and uses electrical signals related to brain activity in order to identify the user’s intention. In this paper a classifier based on a Self-Organizing Map is introduced.MethodsElectroencephalography signal is used on this work as a source for the user’s intention. This signal represents the brain activity and is processed in order to extract the frequency features presented to the classifier, which uses a Self-Organizing Map and a series of probability masks in order to identify the correct class.ResultsThe proposed structure was evaluated using a dataset of Electroencephalography with three mental tasks. The system was able to identify the different states of the users intention with an accuracy of 71.21% for a three-class problem using only 25 neurons for one of the users.ConclusionThe classifier proposed in this paper has an accuracy that is around the value of similar works in the literature, using the same data, but using a small time window for the classification, meaning the system can have a better time response for the user.

Registro de la actividad eléctrica cerebral de la atención implicada en la conducción bajo el efecto del alcohol usando un instrumento BCI (brain control interface) / Using a brain control interface to record brain activity involved in attention during driving under the influence of alcohol

La ingestión de alcohol se ha vinculado con cambios característicos en la actividad EEG, y estos cambios dependen de diversos factores; si bien se reconoce en la literatura una amplia variabilidad de diseños experimentales, la gran mayoría de estos se han centrado en reportar el efecto del alcohol en sujetos alcohólicos con antecedentes de consumo de dosis casi siempre altas y frecuentes, y en un menor porcentaje, el efecto del alcohol cuando hay un consumo agudo de dosis bajas de alcohol. El presente proyecto registró la actividad eléctrica cerebral de la atención implicada en la conducción con el equipo BCI (brain control interface) EPOC, bajo el efecto de 0,300 g de alcohol, correspondiente a un porcentaje de 0,02 % BAC (blood alcohol content) en prueba de alcoholímetro, y a su vez en ausencia de alcohol mediante un diseño pre-experimental con preprueba-postprueba, con un solo grupo de 30 estudiantes universitarios entre 18 y 45 años de edad. Los resultados mostraron que el alcohol en dosis bajas logra generar cambios en la dinámica de las ondas, disminuyendo la amplitud de ondas rápidas como alfa (9-13Hz) y beta (14-30 Hz), específicamente en zonas asociadas a los lóbulos frontales implicadas en tareas de atención sostenida en conducción.

The ingestion of alcohol has been linked to characteristic changes in EEG activity, and these changes depend on several factors. Previous research has been conducted with a variety of experimental designed, but most have focused on reporting the effect of alcohol consumption in subjects with a history of alcohol abuse, and a few have reported the effects of lower doses of alcohol. This project recorded brain activity related to attention in a driving situation with an emotiv EPOP brain control interface (BCI) device after ingestion of 0,300 g of alcohol (0,02 % BAC) or none in a pre-experiemental pre-test and post-test design with 30 college students aged 18-45. Results suggest that lower doses of alcohol change wave dynamics, reducing the amplitude of fast alpha (9-13Hz) and beta (14-30Hz) waves in frontal lobe zones involved in sustained attention in driving.

Brain-computer interface: proposal of a shaping-based training

INTRODUCTION: Persons affected by certain motor disabilities such as amyotrophic lateral sclerosis can evolve with important motor and speech difficulties in communication. A BCI (Brain Computer Interface) is a system that allows interaction between the human brain and a computer, permitting the user to control a communication channel through his or her brain activity. It is based on the analysis and processing of electroencephalographic (EEG) signals to generate control commands. The present study focuses on the subjects' capability to improve the way they learn to control a BCI system. METHODS: Two training procedures were compared: standard and progressive shaping response. Six volunteers participated in a reversal single-subject ABAC design. RESULTS: The study showed that both procedures are equally effective in producing a differential responding in the EEG signals, with no significant differences between them. Nevertheless, there were significant differences when distinguishing two neuronal responses (relax state and hand-movement imagination). Also, in the analysis of individual signals, an adaptive process for the shaping process and a lower error rate in the idle response appeared. CONCLUSION: Both proposed training procedures, standard and progressive shaping, are equally effective to achieve training of differential responses (imagination of hand/relax) in the interaction with a BCI.

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.

Testing aggregation hypotheses among Neotropical trees and shrubs: results from a 50-ha plot over 20 years of sampling

Spatial patterns of tropical trees and shrubs are important to understanding their interaction and the resultant structure of tropical rainforests. To assess this issue, we took advantage of previously collected data, on Neotropical tree and shrub stem identified to species and mapped for spatial coordinates in a 50ha plot, with a frequency of every five years and over a 20 year period. These stems data were first placed into four groups, regardless of species, depending on their location in the vertical strata of the rainforest (shrubs, understory trees, mid-sized trees, tall trees) and then used to generate aggregation patterns for each sampling year. We found shrubs and understory trees clumped at small spatial scales of a few meters for several of the years sampled. Alternatively, mid-sized trees and tall trees did not clump, nor did they show uniform (regular) patterns, during any sampling period. In general (1) groups found higher in the canopy did not show aggregation on the ground and (2) the spatial patterns of all four groups showed similarity among different sampling years, thereby supporting a “shifting mosaic” view of plant communities over large areas. Spatial analysis, such as this one, are critical to understanding and predicting tree spaces, tree-tree replacements and the Neotropical forest patterns, such as biodiversity and those needed for sustainability efforts, they produce.

Con datos obtenidos previamente, se identificaron especies de árboles y arbustos neotropicales y se ubicaron con coordenadas espaciales en una parcela de 50ha cada cinco años durante un período de 20 años. Estos datos primero se dividieron en cuatro grupos según los estratos verticales del bosque (arbustos, árboles del sotobosque, árboles medios y árboles altos); después se usaron tres para estudiar patrones de agregación en cada año de muestreo. Los arbustos y árboles del sotobosque se agruparon en pequeñas escalas espaciales de pocos metros en varios de los años del estudio, mientras que los árboles de tamaño medio y grande no se agregaron ni mostraron patrones regulares en ningún período de muestreo. En general: (1) Las especies más altas del dosel perdieron la agregación en el terreno y (2) Los patrones espaciales de todos los grupos de especies mostraron similitud entre los años de muestreo, lo que apoya la idea de un “mosaico cambiante” de las comunidades vegetales en grandes áreas. El análisis espacial, como este, es fundamental para comprender y predecir los espacios arbóreos, el reemplazo de árbol por árbol y los patrones de los bosques neotropicales, tal como la diversidad y aquellos esfuerzos necesarios para garantizar la sostenibilidad, que producen.

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 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.

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.