Técnicas básicas de electroencefalografía: principios y aplicaciones clínicas

Travieso-González C. Analysis of Brain Computer Interface Using Deep and Machine Learning

Pattern recognition is becoming increasingly important topic in all sectors of society. From the optimization of processes in the industry to the detection and diagnosis of diseases in medicine. Brain-computer interfaces are introduced in this chapter. Systems capable of analyzing brain signal patterns, processing and interpreting them through machine and deep learning algorithms. In this chapter, a hybrid deep/machine learning ensemble system for brain pattern recognition is proposed. It is capable to recognize patterns and translate the decisions to BCI systems. For this, a public database (Physionet) with data of motor tasks and mental tasks is used. The development of this chapter consists of a brief summary of the state of the art, the presentation of the model together with some results and some promising conclusions.

Revisión de electroencefalografía portable y su aplicabilidad en neurociencias

La electroencefalografía (EEG) es una técnica que permite registrar la actividad eléctrica del cerebro y ha sido estudiada durante los últimos cien años en diferentes ámbitos de la neurociencia. En los últimos años se ha investigado y desarrollado equipos de medición que sean portables y que permitan una buena calidad de la señal, por lo cual se realizó una revisión bibliográfica de las compañías fabricantes de algunos dispositivos de electroencefalografía portable disponibles en el mercado, se exponen sus características principales, algunos trabajos encontrados que fueron realizados con los dispositivos, comparaciones entre los mismos y una discusión acerca de las ventajas y desventajas de sus características. Finalmente se concluye que a la hora de comprar un dispositivo para electroencefalografía portable es necesario tener en cuenta el uso que se le va a dar y el costo-beneficio que tiene el equipo de acuerdo con sus características. Encephalography is a technique that allows the recording of electrical activity of the brain and has been studied during the last hundred years in different areas of neuroscience. For several years, measuring equipment that are portable and that allow a good signal quality to have been researched and developed, so a literature review of the manufacturing companies of some of portable electroencephalography devices available on the market was carried out: Its main features are exposed, as well as some of the work found that were made with those, comparisons between them and a discussion about the advantages and disadvantages of their features. It is concluded that, when a portable encephalography device is bought, it’s necessary to take into consideration the use that it will be having and the cost-benefit that the device has according to its features.

Choosing Strategies to Deal with Artifactual EEG Data in Children with Cognitive Impairment

Rett syndrome is a disease that involves acute cognitive impairment and, consequently, a complex and varied symptomatology. This study evaluates the EEG signals of twenty-nine patients and classify them according to the level of movement artifact. The main goal is to achieve an artifact rejection strategy that performs well in all signals, regardless of the artifact level. Two different methods have been studied: one based on the data distribution and the other based on the energy function, with entropy as its main component. The method based on the data distribution shows poor performance with signals containing high amplitude outliers. On the contrary, the method based on the energy function is more robust to outliers. As it does not depend on the data distribution, it is not affected by artifactual events. A double rejection strategy has been chosen, first on a motion signal (accelerometer or EEG low-pass filtered between 1 and 10 Hz) and then on the EEG signal. The results showed a higher performance when working combining both artifact rejection methods. The energy-based method, to isolate motion artifacts, and the data-distribution-based method, to eliminate the remaining lower amplitude artifacts were used. In conclusion, a new method that proves to be robust for all types of signals is designed.