The Riemannian Minimum Distance to Means Field Classifier

International audienc

Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison

Brain computer interfaces (BCIs) have been attracting a great interest in recent years. The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally proposed from a heuristic viewpoint, it can be also built on very strong foundations using information theory. This paper reviews the relationship between CSP and several information-theoretic approaches, including the Kullback–Leibler divergence, the Beta divergence and the Alpha-Beta log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those features that are maximally informative about the class labels. The performance of all the methods will be also compared via experiments.Gobierno Español MICINN TEC2014-53103-

EEG Signal Processing in Motor Imagery Brain Computer Interfaces with Improved Covariance Estimators

Desde hace unos años hasta la actualidad, el desarrollo en el campo de los interfaces cerebro ordenador ha ido aumentando. Este aumento viene motivado por una serie de factores distintos. A medida que aumenta el conocimiento acerca del cerebro humano y como funciona (del que aún se conoce relativamente poco), van surgiendo nuevos avances en los sistemas BCI que, a su vez, sirven de motivación para que se investigue más acerca de este órgano. Además, los sistemas BCI abren una puerta para que cualquier persona pueda interactuar con su entorno independientemente de la discapacidad física que pueda tener, simplemente haciendo uso de sus pensamientos. Recientemente, la industria tecnológica ha comenzado a mostrar su interés por estos sistemas, motivados tanto por los avances con respecto a lo que conocemos del cerebro y como funciona, como por el uso constante que hacemos de la tecnología en la actuali- dad, ya sea a través de nuestros smartphones, tablets u ordenadores, entre otros muchos dispositivos. Esto motiva que compañías como Facebook inviertan en el desarrollo de sistemas BCI para que tanto personas sin discapacidad como aquellas que, si las tienen, puedan comunicarse con los móviles usando solo el cerebro. El trabajo desarrollado en esta tesis se centra en los sistemas BCI basados en movimien- tos imaginarios. Esto significa que el usuario piensa en movimientos motores que son interpretados por un ordenador como comandos. Las señales cerebrales necesarias para traducir posteriormente a comandos se obtienen mediante un equipo de EEG que se coloca sobre el cuero cabelludo y que mide la actividad electromagnética producida por el cere- bro. Trabajar con estas señales resulta complejo ya que son no estacionarias y, además, suelen estar muy contaminadas por ruido o artefactos. Hemos abordado esta temática desde el punto de vista del procesado estadístico de la señal y mediante algoritmos de aprendizaje máquina. Para ello se ha descompuesto el sistema BCI en tres bloques: preprocesado de la señal, extracción de características y clasificación. Tras revisar el estado del arte de estos bloques, se ha resumido y adjun- tado un conjunto de publicaciones que hemos realizado durante los últimos años, y en las cuales podemos encontrar las diferentes aportaciones que, desde nuestro punto de vista, mejoran cada uno de los bloques anteriormente mencionados. De manera muy resumida, para el bloque de preprocesado proponemos un método mediante el cual conseguimos nor- malizar las fuentes de las señales de EEG. Al igualar las fuentes efectivas conseguimos mejorar la estima de las matrices de covarianza. Con respecto al bloque de extracción de características, hemos conseguido extender el algoritmo CSP a casos no supervisados. Por último, en el bloque de clasificación también hemos conseguido realizar una sepa- ración de clases de manera no supervisada y, por otro lado, hemos observado una mejora cuando se regulariza el algoritmo LDA mediante un método específico para Gaussianas.The research and development in the field of Brain Computer Interfaces (BCI) has been growing during the last years, motivated by several factors. As the knowledge about how the human brain is and works (of which we still know very little) grows, new advances in BCI systems are emerging that, in turn, serve as motivation to do more re- search about this organ. In addition, BCI systems open a door for anyone to interact with their environment regardless of the physical disabilities they may have, by simply using their thoughts. Recently, the technology industry has begun to show its interest in these systems, mo- tivated both by the advances about what we know of the brain and how it works, and by the constant use we make of technology nowadays, whether it is by using our smart- phones, tablets or computers, among many other devices. This motivates companies like Facebook to invest in the development of BCI systems so that people (with or without disabilities) can communicate with their devices using only their brain. The work developed in this thesis focuses on BCI systems based on motor imagery movements. This means that the user thinks of certain motor movements that are in- terpreted by a computer as commands. The brain signals that we need to translate to commands are obtained by an EEG device that is placed on the scalp and measures the electromagnetic activity produced by the brain. Working with these signals is complex since they are non-stationary and, in addition, they are usually heavily contaminated by noise or artifacts. We have approached this subject from the point of view of statistical signal processing and through machine learning algorithms. For this, the BCI system has been split into three blocks: preprocessing, feature extraction and classification. After reviewing the state of the art of these blocks, a set of publications that we have made in recent years has been summarized and attached. In these publications we can find the different contribu- tions that, from our point of view, improve each one of the blocks previously mentioned. As a brief summary, for the preprocessing block we propose a method that lets us nor- malize the sources of the EEG signals. By equalizing the effective sources, we are able to improve the estimation of the covariance matrices. For the feature extraction block, we have managed to extend the CSP algorithm for unsupervised cases. Finally, in the classification block we have also managed to perform a separation of classes in an blind way and we have also observed an improvement when the LDA algorithm is regularized by a specific method for Gaussian distributions

Electroencephalograph (EEG) signal processing techniques for motor imagery Brain Computer interface systems

Brain-Computer Interface (BCI) system provides a channel for the brain to control external devices using electrical activities of the brain without using the peripheral nervous system. These BCI systems are being used in various medical applications, for example controlling a wheelchair and neuroprosthesis devices for the disabled, thereby assisting them in activities of daily living. People suffering from Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis and completely locked in are unable to perform any body movements because of the damage of the peripheral nervous system, but their cognitive function is still intact. BCIs operate external devices by acquiring brain signals and converting them to control commands to operate external devices. Motor-imagery (MI) based BCI systems, in particular, are based on the sensory-motor rhythms which are generated by the imagination of body limbs. These signals can be decoded as control commands in BCI application. Electroencephalogram (EEG) is commonly used for BCI applications because it is non-invasive. The main challenges of decoding the EEG signal are because it is non-stationary and has a low spatial resolution. The common spatial pattern algorithm is considered to be the most effective technique for discrimination of spatial filter but is easily affected by the presence of outliers. Therefore, a robust algorithm is required for extraction of discriminative features from the motor imagery EEG signals. This thesis mainly aims in developing robust spatial filtering criteria which are effective for classification of MI movements. We have proposed two approaches for the robust classification of MI movements. The first approach is for the classification of multiclass MI movements based on the thinICA (Independent Component Analysis) and mCSP (multiclass Common Spatial Pattern Filter) method. The observed results indicate that these approaches can be a step towards the development of robust feature extraction for MI-based BCI system. The main contribution of the thesis is the second criterion, which is based on Alpha- Beta logarithmic-determinant divergence for the classification of two class MI movements. A detailed study has been done by obtaining a link between the AB log det divergence and CSP criterion. We propose a scaling parameter to enable a similar way for selecting the respective filters like the CSP algorithm. Additionally, the optimization of the gradient of AB log-det divergence for this application was also performed. The Sub-ABLD (Subspace Alpha-Beta Log-Det divergence) algorithm is proposed for the discrimination of two class MI movements. The robustness of this algorithm is tested with both the simulated and real data from BCI competition dataset. Finally, the resulting performances of the proposed algorithms have been favorably compared with other existing algorithms