Semi-supervised learning with the clustering and Decision Trees classifier for the task of cognitive workload study

The paper is focused on application of the clustering algorithm and Decision Tress classifier (DTs) as a semi-supervised method for the task of cognitive workload level classification. The analyzed data were collected during examination of Digit Symbol Substitution Test (DSST) with use of eye-tracker device. 26 participants took part in examination as volunteers. There were conducted three parts of DSST test with different levels of difficulty. As a results there were obtained three versions of data: low, middle and high level of cognitive workload. The case study covered clustering of collected data by using k-means algorithm to detect three clusters or more. The obtained clusters were evaluated by three internal indices to measure the quality of clustering. The David-Boudin index detected the best results in case of four clusters. Based on this information it is possible to formulate the hypothesis of the existence of four clusters. The obtained clusters were adopted as classes in supervised learning and have been subjected to classification. The DTs was applied in classification. There were obtained the 0.85 mean accuracy for three-class classification and 0.73 mean accuracy for four-class classification. &nbsp

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

Multi-Dimensional Task Recognition for Human-Robot Teaming

Human-robot teams involve humans and robots collaborating to achieve tasks under various environmental conditions. Successful teaming requires robots to adapt autonomously in real-time to a human teammate's state. An important element of such adaptation is the ability for the robot to infer the tasks performed by their human teammates. Human-robot teams often perform a wide variety of tasks, involving multiple activity components, and may even perform two or more tasks concurrently. A robot’s ability to recognize the human’s composite tasks that occur concurrently is a key requirement for realizing successful collaboration. Existing task recognition algorithms are not viable for human-robot teams, as they only detect tasks from a subset of activity components and rarely detect concurrent, composite tasks. This dissertation developed a multi-dimensional task recognition algorithm capable of detecting concurrent, composite tasks across the cognitive, speech, auditory, visual, gross motor, fine-grained motor, and tactile components by incorporating metrics that are sensitive, versatile, and suitable across human-robot teaming paradigms. The developed algorithm addresses a foundational problem of understanding an individual's task engagement state in human-robot teams operating in dynamic, unstructured environments

Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration

One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy

Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration

One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Analysis and extension of hierarchical temporal memory for multivariable time series

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, junio de 201

Applied Cognitive Sciences

Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline