Affective Valence Detection from EEG Signals Using Wrapper Methods

In this work, a novel valence recognition system applied to EEG signals is presented. It consists of a feature extraction block followed by a wrapper classification algorithm. The proposed feature extraction method is based on measures of relative energies computed in short‐time intervals and certain frequency bands of EEG signal segments time‐locked to the stimuli presentation. These measures represent event‐related desynchronization/synchronization of underlying brain neural networks. The subsequent feature selection and classification steps comprise a wrapper technique based on two different classification approaches: an ensemble classifier, i.e., a random forest of classification trees and a support vector machine algorithm. Applying a proper importance measure from the classifiers, the feature elimination has been used to identify the most relevant features of the decision making both for intrasubject and intersubject settings, using single trial signals and ensemble averaged signals, respectively. The proposed methodologies allowed us to identify a frontal region and a beta band as the most relevant characteristics, extracted from the electrical brain activity, in order to determine the affective valence elicited by visual stimuli

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

AUTOMATED ARTIFACT REMOVAL AND DETECTION OF MILD COGNITIVE IMPAIRMENT FROM SINGLE CHANNEL ELECTROENCEPHALOGRAPHY SIGNALS FOR REAL-TIME IMPLEMENTATIONS ON WEARABLES

Electroencephalogram (EEG) is a technique for recording asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. EEG signal is well studied to evaluate the cognitive state, detect brain diseases such as epilepsy, dementia, coma, autism spectral disorder (ASD), etc. In this dissertation, the EEG signal is studied for the early detection of the Mild Cognitive Impairment (MCI). MCI is the preliminary stage of Dementia that may ultimately lead to Alzheimers disease (AD) in the elderly people. Our goal is to develop a minimalistic MCI detection system that could be integrated to the wearable sensors. This contribution has three major aspects: 1) cleaning the EEG signal, 2) detecting MCI, and 3) predicting the severity of the MCI using the data obtained from a single-channel EEG electrode. Artifacts such as eye blink activities can corrupt the EEG signals. We investigate unsupervised and effective removal of ocular artifact (OA) from single-channel streaming raw EEG data. Wavelet transform (WT) decomposition technique was systematically evaluated for effectiveness of OA removal for a single-channel EEG system. Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), is studied with four WT basis functions: haar, coif3, sym3, and bior4.4. The performance of the artifact removal algorithm was evaluated by the correlation coefficients (CC), mutual information (MI), signal to artifact ratio (SAR), normalized mean square error (NMSE), and time-frequency analysis. It is demonstrated that WT can be an effective tool for unsupervised OA removal from single channel EEG data for real-time applications.For the MCI detection from the clean EEG data, we collected the scalp EEG data, while the subjects were stimulated with five auditory speech signals. We extracted 590 features from the Event-Related Potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with Radial Basis Kernel (RBF) (sigma = 10, cost = 102). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI. We also developed a single channel Electro-encephalography (EEG) based MCI severity monitoring algorithm by generating the Montreal Cognitive Assessment (MoCA) scores from the features extracted from EEG. We performed multi-trial and single-trail analysis for the algorithm development of the MCI severity monitoring. We studied Multivariate Regression (MR), Ensemble Regression (ER), Support Vector Regression (SVR), and Ridge Regression (RR) for multi-trial and deep neural regression for the single-trial analysis. In the case of multi-trial, the best result was obtained from the ER. In our single-trial analysis, we constructed the time-frequency image from each trial and feed it to the convolutional deep neural network (CNN). Performance of the regression models was evaluated by the RMSE and the residual analysis. We obtained the best accuracy with the deep neural regression method

On Feature Selection and Rule Extraction for High Dimensional Data: A Case of Diffuse Large B-Cell Lymphomas Microarrays Classification

Neurofuzzy methods capable of selecting a handful of useful features are very useful in analysis of high dimensional datasets. A neurofuzzy classification scheme that can create proper linguistic features and simultaneously select informative features for a high dimensional dataset is presented and applied to the diffuse large B-cell lymphomas (DLBCL) microarray classification problem. The classification scheme is the combination of embedded linguistic feature creation and tuning algorithm, feature selection, and rule-based classification in one neural network framework. The adjustable linguistic features are embedded in the network structure via fuzzy membership functions. The network performs the classification task on the high dimensional DLBCL microarray dataset either by the direct calculation or by the rule-based approach. The 10-fold cross validation is applied to ensure the validity of the results. Very good results from both direct calculation and logical rules are achieved. The results show that the network can select a small set of informative features in this high dimensional dataset. By a comparison to other previously proposed methods, our method yields better classification performance

EEG-Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

The paper aims at detecting on-line cognitive failures in driving by decoding the EEG signals acquired during visual alertness, motor-planning and motor-execution phases of the driver. Visual alertness of the driver is detected by classifying the pre-processed EEG signals obtained from his pre-frontal and frontal lobes into two classes: alert and non-alert. Motor-planning performed by the driver using the pre-processed parietal signals is classified into four classes: braking, acceleration, steering control and no operation. Cognitive failures in motor-planning are determined by comparing the classified motor-planning class of the driver with the ground truth class obtained from the co-pilot through a hand-held rotary switch. Lastly, failure in motor execution is detected, when the time-delay between the onset of motor imagination and the EMG response exceeds a predefined duration. The most important aspect of the present research lies in cognitive failure classification during the planning phase. The complexity in subjective plan classification arises due to possible overlap of signal features involved in braking, acceleration and steering control. A specialized interval/general type-2 fuzzy set induced neural classifier is employed to eliminate the uncertainty in classification of motor-planning. Experiments undertaken reveal that the proposed neuro-fuzzy classifier outperforms traditional techniques in presence of external disturbances to the driver. Decoding of visual alertness and motor-execution are performed with kernelized support vector machine classifiers. An analysis reveals that at a driving speed of 64 km/hr, the lead-time is over 600 milliseconds, which offer a safe distance of 10.66 meters

Sistemas de interfaz cerebro-ordenador basados en dispositivos EEG de bajo coste y modelos neurodifusos aplicados a la imaginación de movimiento

[SPA] La presente tesis doctoral detalla la evaluación de un dispositivo de electroencefalografía de bajo coste a partir de su inclusión en un sistema de interfaz cerebro-máquina completo (BCI del inglés Brain-Computer Interfaces) basado en técnicas neurodifusas. El paradigma elegido se centra en la imaginación de movimiento multiclase sin realimentación, donde la operación es completamente asíncrona. También se ha aportado un avance en el área de la selección de características, desarrollando e implementando una metodología capaz de minimizar las componentes del vector de características necesarias para operar sistemas BCI, facilitando así la integración de éstos en plataformas móviles. En primer lugar, se ha seleccionado el dispositivo Emotiv EPOC en torno a criterios de coste, número de sensores, acceso a la señal capturada, ergonomía y relevancia para la comunidad científica. Del mismo modo, se ha abordado el problema definido en el BCI Competition III dataset V dada la disponibilidad de las señales capturadas sobre el cuero cabelludo por un equipo profesional, la exhaustiva definición del experimento y la facilidad para reproducirlo. Adicionalmente, la existencia de estudios utilizando estos mismos datos ha ofrecido una guía sobre las mejores técnicas a aplicar. Entre éstas destaca el modelo neurodifuso S-dFasArt, que hasta ahora ha presentado los mejores resultados cumpliendo las restricciones del problema. Por tanto, se ha construido un sistema BCI propio utilizando Emotiv EPOC como dispositivo de obtención de la señal EEG y S-dFasArt como sistema de inteligencia artificial. La valoración se ha realizado desde el punto de vista de un sistema BCI completo por lo que, en lugar de examinar la forma de la señal detectada, se ha calculado el rendimiento del mismo para datos capturados con diferentes equipos. Para ello se han comparado los resultados alcanzados tanto a partir de la base de datos BCI Competition como de cuatro conjuntos propios en los que han colaborado 19 voluntarios, quienes han participado en uno o varios experimentos. Se han incluido tanto sesiones utilizando Emotiv EPOC para la obtención de datos, como pruebas donde se ha utilizado una versión híbrida del mismo, en la cual se mantiene la unidad de procesamiento pero varía la tecnología y la ubicación de los sensores. Así, se ha demostrado que el sistema BCI construido integrando Emotiv EPOC junto al clasificador S-dFasArt alcanza una precisión asimilable a la lograda sobre datos capturados por equipos de investigación manteniendo el problema y la posición de los sensores. Además, la ubicación de los mismos sobre la corteza motora ha hecho posible un incremento en torno al 7% en el nivel de acierto medio (desde el 62% al 66.53 %). Igualmente, se ha corroborado la influencia positiva de la realimentación, que ha permitido lograr precisiones de por encima del 70% con Emotiv EPOC. Finamente, se ha presentado una metodología de selección de características en la que el S-dFasArt se ha integrado con modelos basados en combinaciones entre el método estadístico y el criterio difuso con la selección por orden y GMDH. La metodología desarrollada ha seleccionado automáticamente las componentes más relevantes del vector de características, alcanzando el modelo reducido obtenido por las diferentes variantes mejores resultados que el completo para dos de cada tres sujetos. Igualmente, la disminución del tamaño del conjunto de datos es muy significativa, presentando un decremento medio desde 168 a 5 características para la mejor combinación. [ENG] This PhD thesis details the evaluation process of a low-cost electroencephalography device when included into a brain-computer interface system (BCI) based on neuro-fuzzy techniques. The chosen paradigm focuses on the multi-class motor imagery problem, with no feedback and asynchronous operation. Also, a contribution to the feature selection area is presented, developing and implementing a new methodology able to minimise the number of feature vector components required to operate BCI systems, thus facilitating their integration into mobile platforms. First, the Emotiv EPOC EEG device has been selected after performing an economic evaluation considering cost and aspects such as the number of sensors, the available capabilities to access the raw brain data, the ergonomics and the relevance for the scientist community. Likewise, the BCI Competition III Dataset V defined problem has been undertaken. This has been chosen based on the availability of the raw brain signals, the detailed description of the experiment and the ability to reproduce it. Also, the existence of a number of research papers has provided guidance about the best performing approaches tackling this problem. Among then, the S-dFasArt neuro-fuzzy model has shown the best performance following the experiment rules so far. Therefore, a new BCI system has been built using Emotiv EPOC as a data capture device and S-dFasArt as an artificial intelligence unit. This assessment has been performed from the perspective of a complete BCI system so, instead of examining the shape of the detected brainwave, the performance of the setup using different data gathering devices has been analysed. Given that, a comparison of the results obtained has been performed processing data from several databases, including the BCI Competition and other four purposely-built datasets containing brain signals recorded from 19 volunteers participating in one or more experiments. Datasets include scalp potentials recorded using Emotiv EPOC as well as sessions recorded by a hybrid version of it, which maintains the processing unit while integrating a different sensor technology and allowing the setup at different electrode locations. Thus, the BCI system built integrating Emotiv EPOC and the S-dFasArt classiffier has shown an accuracy level comparable to that achieved using research EEG devices for the same problem and sensor locations. Besides, placing the electrodes over the motor cortex has allowed a 7% increase of the average success rate (from 62% to 66.53 %). Additionally, the importance of providing users with live feedback of their performance has been corroborated, obtaining accuracy levels above 70% using Emotiv EPOC. Finally, a new methodology where S- S-dFasArt is integrated with a combination of eit-her the statistic method or the fuzzy criteria and the order selection or GMDH has been introduced. The developed methodology has automatically selected the most relevant components from the feature vector, allowing the reduced model calculated from the different variations to achieve better than original accuracy levels for two out of three subjects. Moreover, a very significant average reduction from 168 to 5 features has been achieved for the highest performing combination.Escuela Internacional de Doctorado de la Universidad Politécnica de CartagenaUniversidad Politécnica de CartagenaPrograma de Doctorado en Tecnologías Industriales por la Universidad Politécnica de Cartagen

Brain signal processing and neurological therapy

Ph.DDOCTOR OF PHILOSOPH

Implicit Interaction with Textual Information using Physiological Signals

Implicit interaction refers to human-computer interaction techniques that do not require active engagement from the users. Instead, the user is passively monitored while performing a computer task, and the data gathered is used to infer implicit measures as inputs to the system. Among the multiple applications for implicit interaction, collecting user feedback on information content is one that has increasingly been investigated. As the amount of available information increases, traditional methods that rely on the users' explicit input become less feasible. As measurement devices become less intrusive, physiological signals arise as a valid approach for generating implicit measures when users interact with information. These signals have mostly been investigated in response to audio-visual content, while it is still unclear how to use physiological signals for implicit interaction with textual information. This dissertation contributes to the body of knowledge by studying physiological signals for implicit interaction with textual information. The research targets three main research areas: a) physiology for implicit relevance measures, b) physiology for implicit affect measures, and c) physiology for real-time implicit interaction. Together, these provide understanding not only on what type of implicit measures can be extracted from physiological signals from users interacting with textual information, but also on how these can be used in real time as part of fully integrated interactive information systems. The first research area targets perceived relevance, as the most noteworthy underlying property regarding the user interaction with information items. Two experimental studies are presented that evaluate the potential for brain activity, electrodermal activity, and facial muscle activity as candidate measures to infer relevance from textual information. The second research area targets affective reactions of the users. The thesis presents two experimental studies that target brain activity, electrodermal activity, and cardiovascular activity to indicate users' affective responses to textual information. The third research area focuses on demonstrating how these measures can be used in a closed interactive loop. The dissertation reports on two systems that use physiological signals to generate implicit measures that capture the user's responses to textual information. The systems demonstrate real-time generation of implicit physiological measures, as well as information recommendation on the basis of implicit physiological measures. This thesis advances the understanding of how physiological signals can be implemented for implicit interaction in information systems. The work calls for researchers and practitioners to consider the use of physiological signals as implicit inputs for improved information delivery and personalization.Implisiittinen vuorovaikutus viittaa ihmisen ja tietokoneen välisen vuorovaikutuksen tekniikoihin, jotka eivät vaadi käyttäjän tarkkaavaisuutta. Tämän sijaan järjestelmä kerää käyttäjästä tietoja passiivisesti ja käyttää näitä tietoja operatiivisina syötteinä. Esimerkiksi viestiä kirjoitettaessa (eksplisiittinen vuorovaikutus) järjestelmä tunnistaa tekemämme kirjoitusvirheen ja automaattisesti korjaa väärin kirjoitetun sanan (implisiittinen vuorovaikutus). Implisiittinen vuorovaikutus mahdollistaa näin uusia vuorovaikutuskanavia vaivaamatta lainkaan käyttäjää. Mittauslaitteiden kehityksen myötä implisiittisessä vuorovaikutuksessa voidaan hyödyntää myös fysiologisia signaaleja, kuten aivovasteita ja kardiovaskulaarisia reaktioita. Näiden signaalien analyysi paljastaa tietoja käyttäjän kiinnostuksen kohteista ja tunteista suhteessa tietokoneen esittämään sisältöön, ja tarjoaa näin järjestelmälle paremmat mahdollisuudet vastata käyttäjän tarpeisiin. Väitöskirjani tarkoituksena on tutkia käyttäjien fysiologisia signaaleja sekä kerätä tietoa heidän reaktioistaan ja mielipiteistään suhteessa tekstipohjaiseen informaatioon ja käyttää näitä signaaleja ja tietoja implisiittisen vuorovaikutuksen mahdollistamiseksi. Tarkkaan ottaen tarkoituksenani on tutkia a) fysiologisten signaalien kykyä kertoa siitä, miten kiinnostavana käyttäjä kokee lukemansa tekstin, b) fysiologisten signaalinen käyttökelpoisuutta ennustamaan, minkälaisia tunnereaktiota (esim. huvittuneisuutta) tekstit herättävät lukijassa sekä, c) fysiologisen signaalien käyttökelpoisuutta reaaliaikaisessa implisiittisessä vuorovaikutuksessa. Tutkimuksen tulokset osoittavat, että fysiologiset signaalit tarjoavat toimivan ratkaisun reaaliaikaiseen implisiittiseen vuorovaikutukseen tekstipohjaisten sisältöjen parissa. Tutkimuksen löydösten pääviesti tutkimusyhteisölle ja alan ammattilaisille on se, että implisiittisinä syötteinä fysiologiset signaalit helpottavat informaation kulkua ja parantavat personalisoimista ihmisen ja tietokoneen välisessä vuorovaikutuksessa

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Sense and Respond

Over the past century, the manufacturing industry has undergone a number of paradigm shifts: from the Ford assembly line (1900s) and its focus on efficiency to the Toyota production system (1960s) and its focus on effectiveness and JIDOKA; from flexible manufacturing (1980s) to reconfigurable manufacturing (1990s) (both following the trend of mass customization); and from agent-based manufacturing (2000s) to cloud manufacturing (2010s) (both deploying the value stream complexity into the material and information flow, respectively). The next natural evolutionary step is to provide value by creating industrial cyber-physical assets with human-like intelligence. This will only be possible by further integrating strategic smart sensor technology into the manufacturing cyber-physical value creating processes in which industrial equipment is monitored and controlled for analyzing compression, temperature, moisture, vibrations, and performance. For instance, in the new wave of the ‘Industrial Internet of Things’ (IIoT), smart sensors will enable the development of new applications by interconnecting software, machines, and humans throughout the manufacturing process, thus enabling suppliers and manufacturers to rapidly respond to changing standards. This reprint of “Sense and Respond” aims to cover recent developments in the field of industrial applications, especially smart sensor technologies that increase the productivity, quality, reliability, and safety of industrial cyber-physical value-creating processes