Eye Blink Classification for Assisting Disability to Communicate Using Bagging and Boosting

Disability is a physical or mental impairment. People with disability have more barriers to do certain activity than those without disability. Moreover, several conditions make them having difficulty to communicate with other people. Currently, researchers have helped people with disabilities by developing brain-computer interface (BCI) technology, which uses artifact on electroencephalograph (EEG) as a communication tool using blinks. Research on eye blinks has only focused on the threshold and peak amplitude, while the difference in how many blinks can be detected using peak amplitude has not been the focus yet. This study used primary data taken using a Muse headband on 15 subjects. This data was used as a dataset classified using bagging (random forest) and boosting (XGBoost) methods with python; 80% of the data was allocated for learning and 20% was for testing. The classified data was divided into ten times of testing, which were then averaged. The number of eye blinks’ classification results showed that the accuracy value using random forest was 77.55%, and the accuracy result with the XGBoost method was 90.39%. The result suggests that the experimental model is successful and can be used as a reference for making applications that help people to communicate by differentiating the number of eye blinks. This research focused on developing the number of eye blinks. However, in this study, only three blinking were used so that further research could increase these number

EMG-based eye gestures recognition for hands free interfacing

This study investigates the utilization of an Electromyography (EMG) based device to recognize five eye gestures and classify them to have a hands free interaction with different applications. The proposed eye gestures in this work includes Long Blinks, Rapid Blinks, Wink Right, Wink Left and finally Squints or frowns. The MUSE headband, which is originally a Brain Computer Interface (BCI) that measures the Electroencephalography (EEG) signals, is the device used in our study to record the EMG signals from behind the earlobes via two Smart rubber sensors and at the forehead via two other electrodes. The signals are considered as EMG once they involve the physical muscular stimulations, which are considered as artifacts for the EEG Brain signals for other studies. The experiment is conducted on 15 participants (12 Males and 3 Females) randomly as no specific groups were targeted and the session was video taped for reevaluation. The experiment starts with the calibration phase to record each gesture three times per participant through a developed Voice narration program to unify the test conditions and time intervals among all subjects. In this study, a dynamic sliding window with segmented packets is designed to faster process the data and analyze it, as well as to provide more flexibility to classify the gestures regardless their duration from one user to another. Additionally, we are using the thresholding algorithm to extract the features from all the gestures. The Rapid Blinks and the Squints were having high F1 Scores of 80.77% and 85.71% for the Trained Thresholds, as well as 87.18% and 82.12% for the Default or manually adjusted thresholds. The accuracies of the Long Blinks, Rapid Blinks and Wink Left were relatively higher with the manually adjusted thresholds, while the Squints and the Wink Right were better with the trained thresholds. However, more improvements were proposed and some were tested especially after monitoring the participants actions from the video recordings to enhance the classifier. Most of the common irregularities met are discussed within this study so as to pave the road for further similar studies to tackle them before conducting the experiments. Several applications need minimal physical or hands interactions and this study was originally a part of the project at HCI Lab, University of Stuttgart to make a hands-free switching between RGB, thermal and depth cameras integrated on or embedded in an Augmented Reality device designed for the firefighters to increase their visual capabilities in the field

EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs

Improving classification of error related potentials using novel feature extraction and classification algorithms for an assistive robotic device

We evaluated the proposed feature extraction algorithm and the classifier, and we showed that the performance surpassed the state of the art algorithms in error detection. Advances in technology are required to improve the quality of life of a person with a severe disability who has lost their independence of movement in their daily life. Brain-computer interface (BCI) is a possible technology to re-establish a sense of independence for the person with a severe disability through direct communication between the brain and an electronic device. To enhance the symbiotic interface between the person and BCI its accuracy and robustness should be improved across all age groups. This thesis aims to address the above-mentioned issue by developing a novel feature extraction algorithm and a novel classification algorithm for the detection of erroneous actions made by either human or BCI. The research approach evaluated the state of the art error detection classifier using data from two different age groups, young and elderly. The performance showed a statistical difference between the aforementioned age groups; therefore, there needs to be an improvement in error detection and classification. The results showed that my proposed relative peak feature (RPF) and adaptive decision surface (ADS) classifier outperformed the state of the art algorithms in detecting errors using EEG for both elderly and young groups. In addition, the novel classification algorithm has been applied to motor imagery to improve the detection of when a person imagines moving a limb. Finally, this thesis takes a brief look at object recognition for a shared control task of identifying utensils in cooperation with a prosthetic robotic hand

Clasificación de características de electroencefalogramas en sistemas Brain Computer Interface basados en ritmos sensoriomotores

Un interfaz cerebro-máquina (BCI) es un modo de comunicación directa entre el cerebro y un dispositivo externo. En este trabajo fin de máster se ha investigado varios métodos para clasificar las señales cerebrales generadas por el usuario y de este modo interpretar su intención. Para ello, previamente se ha realizado un estudio de las investigaciones en el campo de los BCI en las dos últimas décadas. Este estudio se organiza de acuerdo a la estructura típica de un BCI, que está formada por un bloque de adquisición de las señales cerebrales, un bloque de procesamiento de las señales y otro dedicado al control del dispositivo. En primer lugar, se analizaron las diferentes técnicas que los BCI emplean para adquirir la actividad cerebral y los tipos de señales de control que se pueden encontrar en la misma y que pueden ser moduladas voluntariamente por los usuarios. En segundo lugar, se estudiaron las diferentes técnicas utilizadas para el procesamiento de señales cerebrales. Estas técnicas engloban aquéllas que pretenden extraer la información característica de las señales cerebrales y las que emplean esta información extraída para clasificar las señales con el fin de conocer las intenciones del usuario. Por último, se hizo una revisión de los distintos dispositivos que la comunidad científica ha controlado mediante sistemas basados en BCI. A continuación, se estudiaron diferentes métodos de clasificación aplicados a las señales EEG del conjunto de datos 2b de la competición BCI de 2008. El método ganador de dicha competición se basa en un método derivado de Common Spatial Pattern para la extracción de características y emplea como clasificador Naïve Bayesian Parzen Window (NBPW). En este trabajo se han propuesto cuatro métodos de clasificación de características: análisis discriminante lineal, máquina de soporte vectorial, perceptrón multicapa y red probabilística de Parzen. En el resto de etapas del BCI se han mantenido los métodos empleados por el ganador de la competición. Los resultados indican que los clasificadores propuestos como alternativas al NBPW no proporcionan una mejora significativa del rendimiento. La red probabilística de Parzen y SVM consiguen mejorar el rendimiento en 3.8%, el LDA en 1.9% y el perceptrón multicapa no consigue superar el rendimiento de NBPW. Por otro lado, se estudian también otros aspectos relacionados con la etapa de clasificación como es el post-procesado de las probabilidades a posteriori y el tiempo de procesamiento de los clasificadores. El método de post-procesado mejora necesariamente la clasificación de las señales para todos los sujetos. Sin embargo, si que lo hace en promedio para todos los sujetos de prueba. Por último, se ha estudiado el tiempo de computación que necesitan los diferentes algoritmos de clasificación propuestos. En este punto se ha constatado que el método LDA y la red probabilística de Parzen claramente superan al resto clasificando tardan alrededor de medio segundo para procesar todas las señales de test de un sujeto.Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaMáster en Investigación en Tecnologías de la Información y las Comunicacione

Evaluación experimental de aprendizaje máquina extremo aplicado a los sistemas de interfaz cerebro-ordenador basados en imaginación de movimiento

[SPA] Un sistema de interfaz cerebro-ordenador o Brain Computer Interface (BCI) permite controlar dispositivos externos solo con la actividad eléctrica del cerebro. Es decir, permiten enviar comandos al exterior, prescindiendo de todo canal de comunicación muscular. Estos sistemas se desarrollan en varias fases: (i) Adquisición de electroencefalograma (EEG), (ii) Detección de artefactos, (iii) Extracción de características, (iv) Clasificación, (v) Lógica de operación, y (vi) Realimentación. Esta Tesis se centra en dos de estas fases: extracción de características y clasificación. A nivel general, los investigadores han propuesto diversas técnicas para cada una de las fases, con el fin de incrementar el rendimiento en Clasificación de estos sistemas, pero, lo más habitual, es probarlas con señales BCI estándar que se obtienen de repositorios disponibles en Internet, o de usuarios expertos que han sido entrenados para trabajar de forma exitosa con BCI. Por otra parte, en la fase de Clasificación, aún no han sido probadas en profundidad las Máquinas de Aprendizaje Extremo (ELM, Extreme Learning Machine). El objetivo de esta Tesis es evaluar la adecuabilidad del ELM como clasificador para su aplicación a interfaces BCI basadas en la imaginación del movimiento y teniendo en cuenta, tanto sujetos noveles, como expertos. Para ello, se ha utilizado una metodología basada en 2 experimentos. En el primer experimento se han capturado las señales EEG de 5 usuarios noveles. Se ha realizado una extracción de características mediante (PSD) y se han empleado 3 clasificadores (LDA, SVM y ELM). Los valores obtenidos con los clasificadores LDA y SVM se han utilizado como referencia para evaluar el rendimiento del ELM. Los resultados experimentales indican que sí es un método adecuado para clasificar señales de EEG de usuarios noveles, obtenidas bajo experimentos de BCI. l segundo experimento explora el conjunto de características y la configuración del ELM más adecuadas para este tipo de utilización. Para ello, se emplearon señales estándar, extraídas de conjuntos de datos de referencia, que se obtienen de Internet. Se han implementado tres métodos de extracción de características muy usados en BCI como son: PSD; AAR y Hjörth. Hay que tener en cuenta, que los sistemas BCI deben administrar las variaciones a lo largo del tiempo del EEG, ya que las características extraídas no son estacionarias. Por ello, para tomar en consideración la variabilidad de señal EEG, se definieron tres estrategias de combinación de las mismas. La clasificación se realizó de dos formas: a) utilizando las características de cada uno de estos métodos combinados según diferentes estrategias y probando 4 kernels diferentes para el ELM, (lineal, sigmoide, gaussiano y lsg como combinación), y b) utilizando las características de todos los métodos extraídas a la vez y convenientemente combinadas, probando otra vez los 4 kernels para el ELM. Los resultados obtenidos indican que el ELM ofrece bajos valores de adecuabilidad utilizando solo un método de extracción de características, independientemente de las estrategias de combinaciones empleadas y del núcleo implementado. Sin embargo, se observa una mejora de la precisión en la clasificación, mediante el uso de todas las características extraídas al mismo tiempo, adecuadamente combinadas y agrupadas. De esta forma, el ELM se convierte en un método valioso para ser aplicado en sistemas BCI basados en Imaginación de Movimiento.[ENG] A Brain Computer Interface (BCI) system allows to control external devices only by the brain electrical activity. It allows to send commands to the outside world, without any muscular communication channel. These systems are developed in the following phases: (i) EEG acquisition, (ii) Artifact detection, (iii) Extraction of characteristics, (iv) Classification, (v) Logic of operation, and (vi) Feedback. This Thesis focuses on two of these phases: Feature Extraction and Classification. In the characteristics extraction phase the following techniques were implemented: Power Spectrum Density (PSD), Hjorth Parameters (H), Adaptive Autoregressive Coefficients (AAR) and Common Spatial Patterns (CSP). On the other hand, the techniques of Linear Discriminant Analysis (LDA), the Support Vector Machine (SVM) and the nearest K-Neighbours were used in the Classification stage. The researchers propose different techniques for each stage in order to increase the classification performance of these systems, but the more usual is to test them with standard BCI signals obtained from repositories available on the Internet, or from expert users trained to work success- fully with BCI. On the other hand, Extreme Learning Machines (ELMs) have not been thoroughly tested yet in the Classification phase. The aims of this Thesis was to assess the suitability of the ELM as a classifier to be applied to BCI interfaces based on the imagination of motion, taking into account both novel and expert subjects. With this aim, a methodology based on 2 experiments have been applied. In the first experiment the EEG signals of 5 new users were gathered. An extraction of characteristic using (PSD) has been performed and 3 classifiers (LDA, SVM and ELM) have been used. The values obtained from the LDA and SVM serve as reference for evaluating the performance of the ELM. Experimental results indicate that ELM is a suitable method to classify EEG signals from new users, obtained under BCI experiments. The second experiment explores what characteristics set and configuration of the ELM would be suitable for this type of use. Standard signals extracted from Internet reference data sets have been used. Three feature extraction methods widely used in BCI have been implemented: PSD, AAR and Hjorth. It should be noted that BCI systems must manage the EEG variations over time, since the characteristics extracted are not stable, therefore, three strategies were defined to take into account the EEG variability and their combination. The classification was performed in two ways: a) using the characteristics of each of these methods , according to combined different strategies and testing 4 different kernels for the ELM (linear, sigmoid, gaussian and lsg), by using the characteristics of all the methods, extracted at the same time, properly combined and testing again the 4 kernels for the ELM. The results showed low values of reliability of the ELM using only one characteristics extraction method, regardless of the strategies of combination used and the kernel implemented. However, there was an improvement in the accuracy in classification by using all the characteristics, extracted at the same time, appropriately combined and grouped. In this way, the ELM becomes a suitable method to be applied in BCI systems based on Motion Imagery.Escuela Internacional de Doctorado de la Universidad Politécnica de CartagenaUniversidad Politécnica de CartagenaPrograma de Doctorado en Tecnologías Industriales por la Universidad Politécnica de Cartagen

Иностранный язык в контексте проблем профессиональной коммуникации: материалы II Международной научной конференции , 27-29 апреля 2015 г., Томск

Сборник предназначен для специалистов и исследователей в области энергоэффективности и энергосбережения, экологии, инженерного образования, технического перевода, межкультурной коммуникации в сфере профессионального общения