Study of non-invasive cognitive tasks and feature extraction techniques for brain-computer interface (BCI) applications

A brain-computer interface (BCI) provides an important alternative for disabled people that enables the non-muscular communication pathway among individual thoughts and different assistive appliances. A BCI technology essentially consists of data acquisition, pre-processing, feature extraction, classification and device command. Indeed, despite the valuable and promising achievements already obtained in every component of BCI, the BCI field is still a relatively young research field and there is still much to do in order to make BCI become a mature technology. To mitigate the impediments concerning BCI, the study of cognitive task together with the EEG feature and classification framework have been investigated. There are four distinct experiments have been conducted to determine the optimum solution to those specific issues. In the first experiment, three cognitive tasks namely quick math solving, relaxed and playing games have been investigated. The features have been extracted using power spectral density (PSD), logenergy entropy, and spectral centroid and the extracted feature has been classified through the support vector machine (SVM), K-nearest neighbor (K-NN), and linear discriminant analysis (LDA). In this experiment, the best classification accuracy for single channel and five channel datasets were 86% and 91.66% respectively that have been obtained by the PSD-SVM approach. The wink based facial expressions namely left wink, right wink and no wink have been studied through fast Fourier transform (FFT) and sample range feature and then the extracted features have been classified using SVM, K-NN, and LDA. The best accuracy (98.6%) has been achieved by the sample range-SVM based approach. The eye blinking based facial expression has been investigated following the same methodology as the study of wink based facial expression. Moreover, the peak detection approach has also been employed to compute the number of blinks. The optimum accuracy of 99% has been achieved using the peak detection approach. Additionally, twoclass motor imagery hand movement has been classified using SVM, K-NN, and LDA where the feature has been extracted through PSD, spectral centroid and continuous wavelet transform (CWT). The optimum 74.7% accuracy has been achieved by the PSDSVM approach. Finally, two device command prototypes have been designed to translate the classifier output. One prototype can translate four types of cognitive tasks in terms of 5 watts four different colored bulbs, whereas, another prototype may able to control DC motor utilizing cognitive tasks. This study has delineated the implementation of every BCI component to facilitate the application of brainwave assisted assistive appliances. Finally, this thesis comes to the end by drawing the future direction regarding the current issues of BCI technology and these directions may significantly enhance usability for the implementation of commercial applications not only for the disabled but also for a significant number of healthy users

An SSVEP Brain-Computer Interface: A Machine Learning Approach

A Brain-Computer Interface (BCI) provides a bidirectional communication path for a human to control an external device using brain signals. Among neurophysiological features in BCI systems, steady state visually evoked potentials (SSVEP), natural responses to visual stimulation at specific frequencies, has increasingly drawn attentions because of its high temporal resolution and minimal user training, which are two important parameters in evaluating a BCI system. The performance of a BCI can be improved by a properly selected neurophysiological signal, or by the introduction of machine learning techniques. With the help of machine learning methods, a BCI system can adapt to the user automatically. In this work, a machine learning approach is introduced to the design of an SSVEP based BCI. The following open problems have been explored: 1. Finding a waveform with high success rate of eliciting SSVEP. SSVEP belongs to the evoked potentials, which require stimulations. By comparing square wave, triangle wave and sine wave light signals and their corresponding SSVEP, it was observed that square waves with 50% duty cycle have a significantly higher success rate of eliciting SSVEPs than either sine or triangle stimuli. 2. The resolution of dual stimuli that elicits consistent SSVEP. Previous studies show that the frequency bandwidth of an SSVEP stimulus is limited. Hence it affects the performance of the whole system. A dual-stimulus, the overlay of two distinctive single frequency stimuli, can potentially expand the number of valid SSVEP stimuli. However, the improvement depends on the resolution of the dual stimuli. Our experimental results shothat 4 Hz is the minimum difference between two frequencies in a dual-stimulus that elicits consistent SSVEP. 3. Stimuli and color-space decomposition. It is known in the literature that although low-frequency stimuli (\u3c30 Hz) elicit strong SSVEP, they may cause dizziness. In this work, we explored the design of a visually friendly stimulus from the perspective of color-space decomposition. In particular, a stimulus was designed with a fixed luminance component and variations in the other two dimensions in the HSL (Hue, Saturation, Luminance) color-space. Our results shothat the change of color alone evokes SSVEP, and the embedded frequencies in stimuli affect the harmonics. Also, subjects claimed that a fixed luminance eases the feeling of dizziness caused by low frequency flashing objects. 4. Machine learning techniques have been applied to make a BCI adaptive to individuals. An SSVEP-based BCI brings new requirements to machine learning. Because of the non-stationarity of the brain signal, a classifier should adapt to the time-varying statistical characters of a single user\u27s brain wave in realtime. In this work, the potential function classifier is proposed to address this requirement, and achieves 38.2bits/min on offline EEG data

Aportaciones al diseño de interfaces persona-máquina utilizando señales EEG

Las grandes ventajas que brindan los sistemas informáticos y los dispositivos inteligentes, cuya presencia se ha incrementado de manera drástica en los últimos años, resultan inaccesibles para los usuarios que no pueden interactuar con las interfaces convencionales de estos sistemas. Este trabajo se enmarca dentro de las investigaciones realizadas en interfaces alternativas cuyo objetivo es facilitar el acceso de todos los posibles usuarios a estas tecnologías. La tesis doctoral presentada en este documento aborda el diseño de una interfaz cerebro-ordenador, es decir, una interfaz hombre-máquina basada en la adquisición e interpretación de las señales electroencefalográficas, que pretende establecer un canal de comunicación directo entre el cerebro y el ordenador. Partiendo de los registros EEG, generados de manera voluntaria por un usuario al realizar dos tareas mentales relacionadas con la imaginación del movimiento de sus manos, recogidos únicamente en dos electrodos situados sobre la superficie del cuero cabelludo, se propone una arquitectura capaz de reconocer dichas tareas mentales y traducirlas en acciones de un ordenador. En este trabajo, se estudian todas las etapas de una interfaz de este tipo y se realizan aportaciones en cada una de ellas. En la fase de Preprocesamiento, se ha propuesto una arquitectura combinada de la transformada de Fourier y la transformada wavelet. En la fase de extracción y selección de características, se estudian diversas alternativas basadas en la evolución de los algoritmos de análisis de componentes principales y de mínimos cuadrados parciales. Y en la fase de clasificación o traducción, se han propuesto cuatro arquitecturas adaptadas a los métodos de selección y extracción de características presentados, basadas en cálculo de distancias entre señales originales y las recuperadas por el algoritmo de análisis de componentes principales robusto, en el análisis discriminante robusto también sobre las señales transformadas mediante esta misma técnica, en un algoritmo de regresión lineal sobre las componentes latentes obtenidas por el algoritmo de mínimos cuadrados parciales y, finalmente, en las máquinas de vectores soporte. Con el fin de evaluar en profundidad todas las aportaciones, se ha generado una base de datos de señales EEG a partir de los registros de doce usuarios y se ha llevado a cabo un profundo estudio estadístico de todas las alternativas diseñadas y sus parametrizaciones, realizando más de 800000 experimentos hasta llegar a la propuesta de la mejor combinación