Data-driven multivariate and multiscale methods for brain computer interface

This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its noninvasive nature, is mainly considered. The nonlinearity and nonstationarity inherent in EEG and its multichannel recording nature require a new set of data-driven multivariate techniques to estimate more accurately features for enhanced BCI operation. Also, a long term goal is to enable an alternative EEG recording strategy for achieving long-term and portable monitoring. Empirical mode decomposition (EMD) and local mean decomposition (LMD), fully data-driven adaptive tools, are considered to decompose the nonlinear and nonstationary EEG signal into a set of components which are highly localised in time and frequency. It is shown that the complex and multivariate extensions of EMD, which can exploit common oscillatory modes within multivariate (multichannel) data, can be used to accurately estimate and compare the amplitude and phase information among multiple sources, a key for the feature extraction of BCI system. A complex extension of local mean decomposition is also introduced and its operation is illustrated on two channel neuronal spike streams. Common spatial pattern (CSP), a standard feature extraction technique for BCI application, is also extended to complex domain using the augmented complex statistics. Depending on the circularity/noncircularity of a complex signal, one of the complex CSP algorithms can be chosen to produce the best classification performance between two different EEG classes. Using these complex and multivariate algorithms, two cognitive brain studies are investigated for more natural and intuitive design of advanced BCI systems. Firstly, a Yarbus-style auditory selective attention experiment is introduced to measure the user attention to a sound source among a mixture of sound stimuli, which is aimed at improving the usefulness of hearing instruments such as hearing aid. Secondly, emotion experiments elicited by taste and taste recall are examined to determine the pleasure and displeasure of a food for the implementation of affective computing. The separation between two emotional responses is examined using real and complex-valued common spatial pattern methods. Finally, we introduce a novel approach to brain monitoring based on EEG recordings from within the ear canal, embedded on a custom made hearing aid earplug. The new platform promises the possibility of both short- and long-term continuous use for standard brain monitoring and interfacing applications

A Brief Exposition on Brain-Computer Interface

Brain-Computer Interface is a technology that records brain signals and translates them into useful commands to operate a drone or a wheelchair. Drones are used in various applications such as aerial operations, where pilot’s presence is impossible. The BCI can also be used for patients suffering from brain diseases who lose their body control and are unable to move to satisfy their basic needs. By taking advantage of BCI and drone technology, algorithms for Mind-Controlled Unmanned Aerial System can be developed. This paper deals with the classification of BCI & UAV, methodologies of BCI, the framework of BCI, neuro-imaging methods, BCI headset options, BCI platforms, electrode types & their placement, and the result of feature extraction technique (FFT) with 72.5% accuracy

Brain connectivity-patterns representation based on electroencephalography network analysis

Brain connectivity has emerged as a neuronal analysis tool widely used to explore brain functions and supply relevant information in the study of the cognitive processes. However, current methodologies used to assess brain connectivity are not always exact and as a result, possible spurious connections may appear. Moreover, measuring the connection between all possible pairs of EEG-channels leads to high dimensional matrices with either redundant or irrelevant information. To avoid problems in connectivity analysis and issues of high computational cost, a selection stage of the most significant connections can be implemented. Nevertheless, there is not a standard method yet to extract connections and the definition of significant connections may vary accordingly with the object of study. Therefore, to develop an accurate methodology, information inherent to each specific problem should be included. In this work, three different tools are presented, that execute the extraction of significant connections considering the experimental scenario. The first tool, tested on a BCI dataset, finds the set of connections that best discriminate two MI classes. Consequently, a kernel-based methodology of feature selection is used to rank each connection by its contribution in the classes discrimination. Finally, the significant connections will be the smaller set that achieves the best classification accuracy. The second methodology is used in a study of the significant connectivity patterns in attention networks. To this end, the connectivity of two classes (target and non-target) in an oddball paradigm experiment is extracted. Here, the significant connections are selected as the ones that differ the most, statistically speaking, between target and non-target. Finally, in a study of the recovery of a subject with aphasia, differences in connectivity, related to improvements produced by therapy were found. In this study, connections that change through the sessions of treatment at the level of amplitude and structure were extracted. Also, a set of significant connections that changed increasingly between the sessions was selected. For all the proposed methodologies, the brain connectivity is computed over EEG signals and the extraction of the significant connections is based on information inherent to the data or the experiment. In general, the selection of connections allows the considerable reduction of connectivity characteristics, this facilitates the physiological interpretation of the experiments and can improve the performance and computational cost of the systems that use these featuresResumen: La conectividad cerebral se ha convertido en una herramienta de análisis neuronal ampliamente utilizada para explorar funciones cerebrales y proporcionar información relevante en el estudio de los procesos cognitivos. Sin embargo, las metodologías actuales utilizadas para evaluar la conectividad cerebral no siempre son exactas y, como resultado, pueden aparecer posibles conexiones falsas. Además, cuando se mide la conexión entre todos los posibles pares de canales de EEG, se obtienen matrices de alta dimensión con información redundante o irrelevante. Para evitar problemas en el análisis de conectividad y alto costo computacional, se puede implementar una etapa de selección de las conexiones más importantes. Sin embargo, todavía no existe un método estándar para extraer conexiones y la definición de conexiones significativas puede variar de acuerdo con el objeto de estudio. Por lo tanto, para desarrollar una metodología precisa, se debe incluir información inherente a cada problema. En este trabajo, se presentan tres herramientas diferentes que ejecutan una extracción de conexiones significativas considerando el escenario del experimento. La primera herramienta, probada en una base de datos de MI, encuentra el conjunto de conexiones que mejor discrimina dos clases. Para esto se utiliza una metodología de selección de características basada en kernels para asignar un peso de contribución a cada conexión. Finalmente, las conexiones significativas serán en conjunto más pequeño que logre el mejor acierto de clasificación. La segunda metodología, se utiliza en un estudio de los patrones de conectividad significativos en redes de atención. Para esto, se extrae la conectividad de dos clases: target y no target en un experimento de paradigma Oddball. Aquí, las conexiones significativas se seleccionan como las que más se diferencian, estadísticamente, entre target y no target. Finalmente, en un estudio de recuperación de un sujeto con afasia, se encontraron diferencias en la conectividad relacionadas con las mejoras producidas terapia. Conexiones que cambian a través de las sesiones de terapia a nivel de amplitud y de estructura fueron extraídas. Además, se definieron y se seleccionaron como conexiones significativas las cuales tienen un cambio creciente entre las sesiones. Para todas las metodologías propuestas, la conectividad cerebral se calcula sobre señales de EEG y la extracción de las conexiones significativas se basa en información inherente a los datos o el experimento. En general, la selección de conexiones permite una reducción considerable de características de conectividad, esto facilita la interpretación fisiológica de los experimentos y puede mejorar el rendimiento y el costo computacional de los sistemas que utilizan estas característicasMaestrí

Integrated Real-Time Control And Processing Systems For Multi-Channel Near-Infrared Spectroscopy Based Brain Computer Interfaces

This thesis outlines approaches to improve the signal processing and anal- ysis of Near-infrared spectroscopy (NIRS) based brain-computer interfaces (BCI). These approaches were developed in conjunction with the implemen- tation of a new customized exible multi-channel NIRS based BCI hardware system (Soraghan, 2010). Using a comparable functional imaging modality the assumptions on which NIRS-BCI have been reassessed, with regard to cognitive task selection, active area locations and lateralized motor cortex activation separability. This dissertation will also present methods that have been implemented to allow reduced hardware requirements in future NIRS-BCI development. We will also examine the sources of homeostatic physiological interference and present new approaches for analysis and at- tenuation within a real-time NIRS-BCI paradigm

Estimation of single trial ERPs and EEG phase synchronization with application to mental fatigue

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Interfícies cervell-ordinador basades en EEG per a la neurorehabilitació

Una interfície cervell-ordinador, o Brain-Computer Interface (BCI), és un sistema que permet el control de dispositius externs només amb la intenció humana. La capacitat que tenen aquests sistemes de llegir i processar l’activitat del cervell els ha fer uns bons candidats per aplicar-los en el camp de la neurorehabilitació de pacients amb ictus o trastorns de la consciència (Disorders of Consciousness). La base de funcionament del BCI és la detecció de patrons en l’EEG. El paradigma és el seguit d’instruccions que ha de segui l’usuari per generar aquests patrons. Les aplicacions que s’estan desenvolupament en el camp de la neurorehabilitació es basen principalment en fer servir dos paradigmes relacionats amb dos patrons: la imatge motora pel patró ERD/ERS i el paradigma oddball pel patró P300. Una de les conseqüències de l’ictus és la pèrdua de mobilitat d’alguna extremitat. Els BCI basats en la imatge motora poden capturar la intenció de moviment del pacient i activar un dispositiu d’estimulació elèctrica funcional. Aquest dispositiu està connectat a la superfície del braç del pacient a través d’uns elèctrodes, i quan s’activi l’estimulació, provocarà el moviment de l’extremitat afectada. En els pacients de DoC, moltes vegades és difícil determinar el nivell de consciència que tenen, i per avaluar-lo, es fa servir els BCI basats en el paradigma oddball, que consisteix en la presentació d’un estímul repetitiu al pacient, que fa que es generi un tipus de resposta en el seu EEG, i de cop i volta es presentar un estímul totalment diferent, que en cas que hi hagi consciència, la resposta de l’EEG del pacient serà diferent, apareixent el patró P300. El control del BCI per part del pacient és bàsic perquè les teràpies siguin profitoses, i per aconseguir-ho és necessari que el BCI s’adapti a les característiques particular del pacients i de l’entorn en un procés de calibratge previ a la sessió efectiva. Aquest calibratge consisteix en recollir mostres del patró, i com més mostres millor és l’adaptació però més llarg és el temps de calibratge, comportant cansament al pacient i fent que la sessió efectiva acabi sent més curta. En aquest treball s’han aportat millores en el control dels BCIs descrits, i també s’ha desenvolupat un nou mètode que permet superar el compromís del calibratge creant senyals EEG artificials. Els bons resultats d’aquest nou mètode permeten definir línies futures de recerca per millorar el control del BCI per part del pacient, perquè així puguin aprofitar millor les sessions de neurorehabilitació i guanyar qualitat de vida.A Brain-Computer Interface (BCI) is a system that allows controlling an external device using only the human intentions. Its ability to read and process the brain activity has made them good candidates for its application in the neurorehabilitation of stroke and Disorders of Consciousness (DoC) patients. The BCI’s basis is the detection of patterns in the EEG. The paradigm is the list of instructions that the user must follow to generate these patterns. The applications that are being developed in the field of neurorehabilitation are mainly based on using two paradigms related to two patterns: the motor imagery for the ERD / ERS pattern, and the oddball paradigm for the P300 pattern. One of the consequences of stroke is the loss of mobility of some limb. The motor imagery based BCIs can capture the patient’s intention of movement and activate a functional electrical stimulation (FES) device. This device is connected to the patient’s arm through two electrodes, and when the stimulation is activated it will cause the movement of the affected limb. In DoC patients, it is often difficult to assess their level of consciousness. The oddball paradigm BCIs can be used to evaluate the patient consciousness, which involves the presentation of a repetitive stimulus to the patient that provokes a response in the EEG. Suddenly, the BCI presents a totally different stimulus and if the patient is aware, his EEG will be different and a P300 pattern will appear. Patient’s BCI control is essential in order to get effective therapy. To achieve this control, it is necessary that the BCI adapts to the patient’s brain features and to the environment before the session using a calibration process. This process consists of collecting samples from the pattern, then the more samples collected, the longer the calibration time, and the better the adaptation. This longer calibration time can produce tiredness to the patient and a reduction of the rehabilitation session time. In this work, improvements have been introduced in the control of the BCI described, and a new method has been developed to overcome the trade-off in the calibration process by creating artificial EEG signals. The good results of this method lead to the definition of future research to improve the control of BCI by the patients, hence they can take better advantage of neurorehabilitation sessions and enhance their quality of life