Preprocessing by a Bayesian Single-Trial Event-Related Potential Estimation Technique Allows Feasibility of an Assistive Single-Channel P300-Based Brain-Computer Interface

A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives. Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities. Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system’s setup and maintenance by lowering the number N of recording channels. By using offline data collected in 30 subjects (21 amyotrophic lateral sclerosis patients and 9 controls) through a clinical BCI with N=5 channels, in the present paper we show that a preprocessing approach based on a Bayesian single-trial ERP estimation technique allows reducing N to 1 without affecting the system’s accuracy. The potentially great benefit for the practical usability of BCI devices (including patient acceptance) that would be given by the reduction of the number N of channels encourages further development of the present study, for example, in an online setting

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

EEG signal classification for MI-BCI applications

Una Interfaz Cerebro Ordenador (o BCI por sus siglas en inglés) es un sistema que permite al usuario producir instrucciones que pueden ser interpretadas por una máquina sin necesidad de interacción física. Funciona como un puente entre el cerebro humano y un ordenador y es una solución prometedora que permitiría a personas con discapacidad motora interactuar con sus alrededores de una nueva manera, mejorando drásticamente su calidad de vida. Existen múltiples tipos de BCI que se basan en diferentes señales cerebrales que pueden ser registradas con distintos dispositivos. Entre estas BCI, una de las más interesantes es la Interfaz Cerebro Ordenador de Movimiento Imaginario (MI-BCI): el usuario piensa en efectuar un movimiento repetidamente y el sistema tiene que identificar de qué movimiento se trata de entre un conjunto de posibilidades, por ejemplo, movimientos de mano izquierda y derecha, pies o lengua. Este sistema se basa en que, cuando una persona se imagina a sí misma haciendo un movimiento, se activa una respuesta en el cerebro similar a la que se produce cuando el movimiento se realiza de verdad. Esta actividad cerebral se puede registrar de múltiples maneras; sin embargo, el transductor más común usado para grabar señales cerebrales es el Electroencefalógrafo (EEG), que es un sistema económico, portable y no invasivo que se compone de múltiples electrodos colocados en el cuero cabelludo del usuario y conectados a un amplificador. El problema presentado no es sencillo. Las señales de EEG tienen una baja resolución espacial, baja relación señal a ruido y se ven afectadas por interferencias producidas por movimiento ocular o muscular entre otros. Además, se introduce redundancia porque estas señales se propagan a través de las diferentes capas que componen la cabeza y llegan a la vez a varios electrodos. Como resultado, debemos emplear múltiples potentes técnicas de procesado de señal para obtener un sistema MI-BCI robusto. Comenzamos introduciendo brevemente algunas BCI existentes, así como el conjunto de datos que analizaremos en este documento, que es el MI-BCI dataset 2a, de la BCI Competition IV. Este está disponible para uso público y ha sido estudiado previamente en profundidad. A continuación, presentamos el problema de MI-BCI como un problema general de reconocimiento de patrones y lo dividimos en sus partes principales: preprocesado de señales, extracción de características o reducción de dimensionalidad y finalmente clasificación, que será el centro de atención de este trabajo. Las primeras dos fases preparan las señales y extraen de ellas la información más relevante, mientras que en la última se realiza la predicción. Para este paso una solución que se ha usado extensivamente en el pasado ha sido el Análisis de Discriminantes Lineales (LDA), pero también existen otras alternativas. En el siguiente capítulo intentamos ilustrar el funcionamiento interno de algunos de estos clasificadores y finalmente realizamos un experimento para comparar los resultados obtenidos con los diferentes algoritmos en el problema de MI-BCI. Antes de realizar ninguna predicción, los clasificadores pasan por una fase de entrenamiento en la que se calculan los parámetros internos que gobiernan su comportamiento. Esto se realiza con observaciones similares a aquellas que después se desean clasificar. En este documento el problema no solo se aborda desde la clásica situación supervisada, en la que durante el entrenamiento la información sobre cuáles eran las intenciones del usuario es conocida, sino que también se trata el caso no supervisado, en el que esta información se desconoce.A Brain Computer Interface (BCI) is a system that allows the user to produce commands that can be interpreted by a machine without the need for physical interaction. It acts as a bridge between a human brain and a computer, and it is a promising solution that would allow people with motor disabilities to interact with their surroundings in a new way, drastically improving their quality of life. Several different types of BCI exist, based on multiple distinct brain signals that can be registered with different physical devices. Amongst these BCIs, one of the most interesting is Motor Imagery BCI (MI-BCI). The user thinks about performing a movement repeatedly, and the system has to identify which movement the person is imagining amongst a defined set of possibilities, for example left and right hand, feet or tongue movements. The working principle behind this is that when a person imagines themselves performing a movement, some of the same responses that happen when the movement is actually performed are triggered. This brain activity can be registered in multiple ways, however, the most common transducer used to record brain signals is the Electroencefalogram (EEG), which is a portable, affordable and non invasive system composed by electrodes placed on the scalp of the subject and attached to an amplifier. The problem presented here is not simple. The EEG signals have low spatial resolution, low signal to noise ratio and are affected by artifacts produced by eye or muscle movement. Furthermore, redundancy is introduced, as the same signals propagate through the different layers of the head and reach multiple electrodes at the same time. As a result, we must use several powerful signal processing techniques in order to obtain a robust MI-BCI. To begin with, we introduce some of the different existing BCIs briefly, as well as the data set that will be analysed in this work, which is the publicly available and extensively studied MI-BCI dataset 2a from the BCI Competition IV. Next, we present the MI-BCI problem as a general pattern recognition problem, and divide it into its main parts, signal preprocessing, feature extraction or dimensionality reduction and finally classification, the main focus of this work. The first two stages prepare the signals and extract the most relevant information in them, whereas in the latter the prediction is made. For the classifying step, a solution known as Linear Discriminant Analysis (LDA) has been widely used previously, however, other less explored methods do exist. In the next chapter, we strive to provide some insight into the principles in which this classifier as well as other less common approaches are sustained, and finally we perform an experiment in order to compare the different solutions that these algorithms achieve when used in a MI-BCI problem. Prior to making any predictions, a training stage is needed, in which the internal parameters that govern the behaviour of the algorithm are obtained. These parameters are calculated using observations similar to those that we will be required to classify. We study not only the classic supervised situation in which during training the intentions of the user are known, but we aim to expand the problem to the less studied unsupervised scenario, where this information is not provided.Universidad de Sevilla. Grado en Ingeniería de las Tecnologías de Telecomunicació

Bayesian machine learning applied in a brain-computer interface for disabled users

A brain-computer interface (BCI) is a system that enables control of devices or communication with other persons, only through cerebral activity, without using muscles. The main application for BCIs is assistive technology for disabled persons. Examples for devices that can be controlled by BCIs are artificial limbs, spelling devices, or environment control systems. BCI research has seen renewed interest in recent years, and it has been convincingly shown that communication via a BCI is in principle feasible. However, present day systems still have shortcomings that prevent their widespread application. In part, these shortcomings are caused by limitations in the functionality of the pattern recognition algorithms used for discriminating brain signals in BCIs. Moreover, BCIs are often tested exclusively with able-bodied persons instead of conducting tests with the target user group, namely disabled persons. The goal of this thesis is to extend the functionality of pattern recognition algorithms for BCI systems and to move towards systems that are helpful for disabled users. We discuss extensions of linear discriminant analysis (LDA), which is a simple but efficient method for pattern recognition. In particular, a framework from Bayesian machine learning, the so-called evidence framework, is applied to LDA. An algorithm is obtained that learns classifiers quickly, robustly, and fully automatically. An extension of this algorithm allows to automatically reduce the number of sensors needed for acquisition of brain signals. More specifically, the algorithm allows to perform electrode selection. The algorithm for electrode selection is based on a concept known as automatic relevance determination (ARD) in Bayesian machine learning. The last part of the algorithmic development in this thesis concerns methods for computing accurate estimates of class probabilities in LDA-like classifiers. These probabilities are used to build a BCI that dynamically adapts the amount of acquired data, so that a preset, approximate bound on the probability of misclassifications is not exceeded. To test the algorithms described in this thesis, a BCI specifically tailored for disabled persons is introduced. The system uses electroencephalogram (EEG) signals and is based on the P300 evoked potential. Datasets recorded from five disabled and four able-bodied subjects are used to show that the Bayesian version of LDA outperforms plain LDA in terms of classification accuracy. Also, the impact of different static electrode configurations on classification accuracy is tested. In addition, experiments with the same datasets demonstrate that the algorithm for electrode selection is computationally efficient, yields physiologically plausible results, and improves classification accuracy over static electrode configurations. The classification accuracy is further improved by dynamically adapting the amount of acquired data. Besides the datasets recorded from disabled and able-bodied subjects, benchmark datasets from BCI competitions are used to show that the algorithms discussed in this thesis are competitive with state-of-the-art electroencephalogram (EEG) classification algorithms. While the experiments in this thesis are uniquely performed with P300 datasets, the presented algorithms might also be useful for other types of BCI systems based on the EEG. This is the case because functionalities such as robust and automatic computation of classifiers, electrode selection, and estimation of class probabilities are useful in many BCI systems. Seen from a more general point of view, many applications that rely on the classification of cerebral activity could possibly benefit from the methods developed in this thesis. Among the potential applications are interrogative polygraphy ("lie detection") and clinical applications, for example coma outcome prognosis and depth of anesthesia monitoring

Bayesian Inference on Brain-Computer Interfaces via GLASS

Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electroencephalogram (EEG) signals. However, the low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of EEG signals present challenges in modeling and computation, especially for individuals with severe physical disabilities-BCI's primary users. To address these challenges, we introduce a novel Gaussian Latent channel model with Sparse time-varying effects (GLASS) under a fully Bayesian framework. GLASS is built upon a constrained multinomial logistic regression particularly designed for the imbalanced target and non-target stimuli. The novel latent channel decomposition efficiently alleviates strong spatial correlations between EEG channels, while the soft-thresholded Gaussian process (STGP) prior ensures sparse and smooth time-varying effects. We demonstrate GLASS substantially improves BCI's performance in participants with amyotrophic lateral sclerosis (ALS) and identifies important EEG channels (PO8, Oz, PO7, and Pz) in parietal and occipital regions that align with existing literature. For broader accessibility, we develop an efficient gradient-based variational inference (GBVI) algorithm for posterior computation and provide a user-friendly Python module available at https://github.com/BangyaoZhao/GLASS.Comment: 32 pages, 5 figure

xDAWN algorithm to enhance evoked potentials: application to brain-computer interface.

International audienceA brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin . An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected into the estimated signal subspace. Data recorded on three subjects were used to evaluate the proposed method. The results, which are presented using a Bayesian linear discriminant analysis classifier , show that the proposed method is efficient and accurate