Under-sampling and Classification of P300 Single-Trials using Self-Organized Maps and Deep Neural Networks for a Speller BCI

A Brain-Computer Interface (BCI) allows its userto control machines or other devices by translating its brainactivity and using it as commands. This kind of technologyhas as potential users people with motor disabilities since itwould allow them to interact with their environment withoutusing their peripheral nerves, helping them to regain their lostautonomy. One of the most successful BCI applications is theP300-based Speller. Its operation depends entirely on its capacityto identify and discriminate the presence of the P300 potentialsfrom electroencephalographic (EEG) signals. For the system to dothis correctly, it is necessary to choose an adequate classifier andtrain it with a balanced data-set. However, due to the use of anoddball paradigm to elicit the P300 potential, only unbalanceddata-sets can be obtained. This paper focuses on the trainingstage of two classifiers, a deep feedforward network (DFN) anda deep belief network (DBN), to be used in a P300-based BCI. Thedata-sets obtained from healthy subjects and post-stroke victimswere pre-processed and then balanced using a Self-OrganizingMaps-based under-sampling approach prior training looking toincrease the accuracy of the classifiers. We compared the resultswith our previous works and observed an increase of 7% inclassification accuracy for the most critical subject. The DFNachieved a maximum classification accuracy of 93.29% for apost-stroke subject and 93.60% for a healthy one

Single-trial P300 classification using deep belief networks for a BCI system

A brain-computer interface (BCI) aims to provide its users with the capability to interact with machines only through its brain activity. There is a special interest in developing BCIs targeted at people with mild or severe motor disabilities since this kind of technology would improve their lifestyles. The Speller is a BCI application that uses the P300 waveform to essentially allow its user to communicate without using its peripheral nerves. This paper focuses on the classification of the P300 waveform from single-trials obtained through EEG using deep belief networks (DBNs). This deep learning algorithm can identify relevant features automatically from the subject's data, making its training requiring less pre-processing stages. The network was tested using signals recorded from healthy subjects and post-stroke victims. The highest accuracy achieved was of 91.6% for a healthy subject and 88.1% for a post-stroke victim

Study of non-invasive cognitive tasks and feature extraction techniques for brain-computer interface (BCI) applications

A brain-computer interface (BCI) provides an important alternative for disabled people that enables the non-muscular communication pathway among individual thoughts and different assistive appliances. A BCI technology essentially consists of data acquisition, pre-processing, feature extraction, classification and device command. Indeed, despite the valuable and promising achievements already obtained in every component of BCI, the BCI field is still a relatively young research field and there is still much to do in order to make BCI become a mature technology. To mitigate the impediments concerning BCI, the study of cognitive task together with the EEG feature and classification framework have been investigated. There are four distinct experiments have been conducted to determine the optimum solution to those specific issues. In the first experiment, three cognitive tasks namely quick math solving, relaxed and playing games have been investigated. The features have been extracted using power spectral density (PSD), logenergy entropy, and spectral centroid and the extracted feature has been classified through the support vector machine (SVM), K-nearest neighbor (K-NN), and linear discriminant analysis (LDA). In this experiment, the best classification accuracy for single channel and five channel datasets were 86% and 91.66% respectively that have been obtained by the PSD-SVM approach. The wink based facial expressions namely left wink, right wink and no wink have been studied through fast Fourier transform (FFT) and sample range feature and then the extracted features have been classified using SVM, K-NN, and LDA. The best accuracy (98.6%) has been achieved by the sample range-SVM based approach. The eye blinking based facial expression has been investigated following the same methodology as the study of wink based facial expression. Moreover, the peak detection approach has also been employed to compute the number of blinks. The optimum accuracy of 99% has been achieved using the peak detection approach. Additionally, twoclass motor imagery hand movement has been classified using SVM, K-NN, and LDA where the feature has been extracted through PSD, spectral centroid and continuous wavelet transform (CWT). The optimum 74.7% accuracy has been achieved by the PSDSVM approach. Finally, two device command prototypes have been designed to translate the classifier output. One prototype can translate four types of cognitive tasks in terms of 5 watts four different colored bulbs, whereas, another prototype may able to control DC motor utilizing cognitive tasks. This study has delineated the implementation of every BCI component to facilitate the application of brainwave assisted assistive appliances. Finally, this thesis comes to the end by drawing the future direction regarding the current issues of BCI technology and these directions may significantly enhance usability for the implementation of commercial applications not only for the disabled but also for a significant number of healthy users

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

Integración de un sistema robótico asistencial controlado mediante una interfaz cerebro computador para personas con discapacidad motora

La calidad de vida de una persona que sufre alguna discapacidad motora hoy en día depende de muchos factores. Entre ellos los más importantes son el factor económico, familiar y emocional. La discapacidad motora es la incapacidad de controlar los músculos del cuerpo. Esta hace de la persona con discapacidad dependiente de otra persona. En la actualidad existen tratamientos físicos, psicológicos que contribuyen a mejorar la calidad de vida, pero esto no evita la que sigan siendo dependientes de otras personas. En este contexto, la presente tesis, desarrolla la implementación de un sistema robótico que devuelve la autonomía parcial a una persona con discapacidad motora para permitir a las personas realizar algunas labores cotidianas. En la presente tesis se describe entonces las tecnologías, mecánica, eléctrica, control e informática para la correcta implementación del sistema robótico, donde las principales partes del sistema son: brazo robótico, casco con electrodos pasivos, pantalla de interacción, cámaras HD. Con el fin de determinar si la implementación de sistema robótico tuvo éxito, se realizan pruebas con personas sanas, teniendo resultados satisfactorios después de las sesiones de entrenamiento y de experiencia con el sistema robótico.Tesi

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Une approche logicielle du traitement de la dyslexie : étude de modèles et applications

Neuropsychological disorders are widespread and generate real public health problems. In particular in our modern society, where written communication is ubiquitous, dyslexia can be extremely disabling. Nevertheless we can note that the diagnosis and remediation of this pathology are fastidious and lack of standardization. Unfortunately it seems inherent to the clinical characterization of dyslexia by exclusion, to the multitude of different practitioners involved in such treatment and to the lack of objectivity of some existing methods. In this respect, we decided to investigate the possibilities offered by modern computing to overcome these barriers. Indeed we have assumed that the democratization of computer systems and their computing power could make of them a perfect tool to alleviate the difficulties encountered in the treatment of dyslexia. This research has led us to study the techniques software as well as hardware, which can conduct to the development of an inexpensive and scalable system able to attend a beneficial and progressive changing of practices in this pathology field. With this project we put ourselves definitely in an innovative stream serving quality of care and aid provided to people with disabilities. Our work has been identifying different improvement areas that the use of computers enables. Then each of these areas could then be the subject of extensive research, modeling and prototype developments. We also considered the methodology for designing this kind of system as a whole. In particular our thoughts and these accomplishments have allowed us to define a software framework suitable for implementing a software platform that we called the PAMMA. This platform should theoretically have access to all the tools required for the flexible and efficient development of medical applications integrating business processes. In this way it is expected that this system allows the development of applications for caring dyslexic patients thus leading to a faster and more accurate diagnosis and a more appropriate and effective remediation. Of our innovation efforts emerge encouraging perspectives. However such initiatives can only be achieved within multidisciplinary collaborations with many functional, technical and financial means. Creating such a consortium seems to be the next required step to get a funding necessary for realizing a first functional prototype of the PAMMA, as well as its first applications. Some clinical studies may be conducted to prove undoubtedly the effectiveness of such an approach for treating dyslexia and eventually other neuropsychological disorders.Les troubles neuropsychologiques sont très répandus et posent de réels problèmes de santé publique. En particulier, dans notre société moderne où la communication écrite est omniprésente, la dyslexie peut s’avérer excessivement handicapante. On remarque néanmoins que le diagnostic et la remédiation de cette pathologie restent délicats et manquent d’uniformisation. Ceci semble malheureusement inhérent à la caractérisation clinique par exclusion de la dyslexie, à la multitude de praticiens différents impliqués dans une telle prise en charge ainsi qu’au manque d’objectivité de certaines méthodes existantes. A ce titre, nous avons décidé d’investiguer les possibilités offertes par l’informatique actuelle pour surmonter ces barrières. Effectivement, nous avons supposé que la démocratisation des systèmes informatiques et leur puissance de calcul pourraient en faire un outil de choix pour pallier les difficultés rencontrées lors de la prise en charge de la dyslexie. Cette recherche nous a ainsi mené à étudier les techniques, aussi bien logicielles que matérielles, pouvant conduire au développement d’un système bon marché et évolutif qui serait capable d’assister un changement bénéfique et progressif des pratiques qui entourent cette pathologie. Avec ce projet, nous nous plaçons définitivement dans un courant innovant au service de la qualité des soins et des aides apportées aux personnes souffrant d’un handicap. Notre travail a ainsi consisté à identifier différents axes d’amélioration que l’utilisation de l’outil informatique rend possible. Chacun de ces axes a alors pu faire l’objet de recherches exhaustives, de modélisations et de développements de prototypes. Nous avons également réfléchi à la méthodologie à mettre en œuvre pour concevoir un tel système dans sa globalité. En particulier, nos réflexions et ces différents accomplissements nous ont permis de définir un framework logiciel propice à l’implémentation d’une plate-forme logicielle que nous avons appelée la PAMMA. Cette plate-forme devrait théoriquement pouvoir disposer de tous les outils permettant le développement souple et efficace d’applications médicales intégrant des processus métiers. Il est ainsi attendu de ce système qu’il permette le développement d’applications, pour la prise en charges des patients dyslexiques, conduisant à un diagnostic plus rapide et plus précis ainsi qu’à une remédiation plus adaptée et plus efficace. De notre effort d’innovation ressortent des perspectives encourageantes. Cependant, ce type d’initiative ne peut se concrétiser qu’autour de collaborations pluridisciplinaires disposant de nombreux moyens fonctionnels, techniques et financiers. La constitution d’un tel consortium semble donc être la prochaine étape nécessaire à l’obtention des financements pour réaliser un premier prototype fonctionnel de la PAMMA, ainsi que de premières applications. Des études cliniques pourront être alors menées pour prouver indubitablement l’efficacité d’une telle approche dans le cadre de la prise en charge de la dyslexie, ainsi qu’éventuellement d’autres troubles neuropsychologiques