A brain-computer interface for potential non-verbal facial communication based on EEG signals related to specific emotions

Unlike assistive technology for verbal communication, the brain–machine or brain–computer interface (BMI/BCI) has not been established as a nonverbal communication tool for amyotrophic lateral sclerosis (ALS) patients. Face-to-face communication enables access to rich emotional information, but individuals suffering from neurological disorders, such as ALS and autism, may not express their emotions or communicate their negative feelings. Although emotions may be inferred by looking at facial expressions, emotional prediction for neutral faces necessitates advanced judgment. The process that underlies brain neuronal responses to neutral faces and causes emotional changes remains unknown. To address this problem, therefore, this study attempted to decode conditioned emotional reactions to neutral face stimuli. This direction was motivated by the assumption that if electroencephalogram (EEG) signals can be used to detect patients’ emotional responses to specific inexpressive faces, the results could be incorporated into the design and development of BMI/BCI-based nonverbal communication tools. To these ends, this study investigated how a neutral face associated with a negative emotion modulates rapid central responses in face processing and then identified cortical activities. The conditioned neutral face-triggered event-related potentials that originated from the posterior temporal lobe statistically significantly changed during late face processing (600–700 ms) after stimulus, rather than in early face processing activities, such as P1 and N170 responses. Source localization revealed that the conditioned neutral faces increased activity in the right fusiform gyrus. This study also developed an efficient method for detecting implicit negative emotional responses to specific faces by using EEG signals

Efficiency evaluation of external environments control using bio-signals

There are many types of bio-signals with various control application prospects. This dissertation regards possible application domain of electroencephalographic signal. The implementation of EEG signals, as a source of information used for control of external devices, became recently a growing concern in the scientific world. Application of electroencephalographic signals in Brain-Computer Interfaces (BCI) (variant of Human-Computer Interfaces (HCI)) as an implement, which enables direct and fast communication between the human brain and an external device, has become recently very popular. Currently available on the market, BCI solutions require complex signal processing methodology, which results in the need of an expensive equipment with high computing power. In this work, a study on using various types of EEG equipment in order to apply the most appropriate one was conducted. The analysis of EEG signals is very complex due to the presence of various internal and external artifacts. The signals are also sensitive to disturbances and non-stochastic, what makes the analysis a complicated task. The research was performed on customised (built by the author of this dissertation) equipment, on professional medical device and on Emotiv EPOC headset. This work concentrated on application of an inexpensive, easy to use, Emotiv EPOC headset as a tool for gaining EEG signals. The project also involved application of embedded system platform - TS-7260. That solution caused limits in choosing an appropriate signal processing method, as embedded platforms characterise with a little efficiency and low computing power. That aspect was the most challenging part of the whole work. Implementation of the embedded platform enables to extend the possible future application of the proposed BCI. It also gives more flexibility, as the platform is able to simulate various environments. The study did not involve the use of traditional statistical or complex signal processing methods. The novelty of the solution relied on implementation of the basic mathematical operations. The efficiency of this method was also presented in this dissertation. Another important aspect of the conducted study is that the research was carried out not only in a laboratory, but also in an environment reflecting real-life conditions. The results proved efficiency and suitability of the implementation of the proposed solution in real-life environments. The further study will focus on improvement of the signal-processing method and application of other bio-signals - in order to extend the possible applicability and ameliorate its effectiveness

Emotion Analysis on EEG Signal Using Machine Learning and Neural Network

Emotion has a significant influence on how one thinks and interacts with others. It serves as a link between how a person feels and the actions one takes, or it could be said that it influences one's life decisions on occasion. Since the patterns of emotions and their reflections vary from person to person, their inquiry must be based on approaches that are effective over a wide range of population regions. To extract features and enhance accuracy, emotion recognition using brain waves or EEG signals requires the implementation of efficient signal processing techniques. Various approaches to human-machine interaction technologies have been ongoing for a long time, and in recent years, researchers have had great success in automatically understanding emotion using brain signals. In our research, several emotional states were classified and tested on EEG signals collected from a well-known publicly available dataset, the DEAP Dataset, using SVM (Support Vector Machine), KNN (K-Nearest Neighbor), and an advanced neural network model, RNN (Recurrent Neural Network), trained with LSTM (Long Short Term Memory). The main purpose of this study is to improve ways to improve emotion recognition performance using brain signals. Emotions, on the other hand, can change with time. As a result, the changes in emotion over time are also examined in our research

Collaborative Learning in Computer Vision

The science of designing machines to extract meaningful information from digital images, videos, and other visual inputs is known as Computer Vision (CV). Deep learning algorithms cope CV problems by automatically learning task-specific features. Especially, Deep Neural Networks (DNNs) have become an essential component in CV solutions due to their ability to encode large amounts of data and capacity to manipulate billions of model parameters. Unlike machines, humans learn by rapidly constructing abstract models. This is undoubtedly due to the fact that good teachers supply their students with much more than just the correct answer; they also provide intuitive comments, comparisons, and explanations. In deep learning, the availability of such auxiliary information at training time (but not at test time) is referred to as learning by Privileged Information (PI). Typically, predictions (e.g., soft labels) produced by a bigger and better network teacher are used as structured knowledge to supervise the training of a smaller network student, helping the student network to generalize better than that trained from scratch. This dissertation focuses on the category of deep learning systems known as Collaborative Learning, where one DNN model helps other models or several models help each other during training to achieve strong generalization and thus high performance. The question we address here is thus the following: how can we take advantage of PI for training a deep learning model, knowing that, at test time, such PI might be missing? In this context, we introduce new methods to tackle several challenging real-world computer vision problems. First, we propose a method for model compression that leverages PI in a teacher-student framework along with customizable block-wise optimization for learning a target-specific lightweight structure of the neural network. In particular, the proposed resource-aware optimization is employed on suitable parts of the student network while respecting the expected resource budget (e.g., floating-point operations per inference and model parameters). In addition, soft predictions produced by the teacher network are leveraged as a source of PI, forcing the student to preserve baseline performance during network structure optimization. Second, we propose a multiple-model learning method for action recognition, specifically devised for challenging video footages in which actions are not explicitly visualized, but rather, only implicitly referred. We use such videos as stimuli and involve a large sample of subjects to collect a high-definition EEG and video dataset. Next, we employ collaborative learning in a multi-modal setting i.e., the EEG (teacher) model helps the video (student) model by distilling the knowledge (implicit meaning of visual stimuli) to it, sharply boosting the recognition performance. The goal of Unsupervised Domain Adaptation (UDA) methods is to use the labeled source together with the unlabeled target domain data to train a model that generalizes well on the target domain. In contrast, we cast UDA as a pseudo-label refinery problem in the challenging source-free scenario i.e., in cases where the source domain data is inaccessible during training. We propose Negative Ensemble Learning (NEL) technique, a unified method for adaptive noise filtering and progressive pseudo-label refinement. In particular, the ensemble members collaboratively learn with a Disjoint Set of Residual Labels, an outcome of the output prediction consensus, to refine the challenging noise associated with the inferred pseudo-labels. A single model trained with the refined pseudo-labels leads to superior performance on the target domain, without using source data samples at all. We conclude this dissertation with a method extending our previous study by incorporating Continual Learning in the Source-Free UDA. Our new method comprises of two stages: a Source-Free UDA pipeline based on pseudo-label refinement, and a procedure for extracting class-conditioned source-style images by leveraging the pre-trained source model. While stage 1 holds the same collaborative peculiarities, in stage 2, the collaboration exists in an indirect manner i.e., it is the source model that provides the only possibility to generate source-style synthetic images which eventually helps the final model in preserving good performance on both source and target domains. In each study, we consider heterogeneous CV tasks. Nevertheless, with an extensive pool of experiments on various benchmarks carrying diverse complexities and challenges, we show that the collaborative learning framework outperforms the related state-of-the-art methods by a considerable margin

Análise do testemunho ocular utilizando sinais de eletroencefalograma

The application of Brain Computer Interfaces techniques to vital crime witnesses could and probably will be a key feature in the justice system. Features from the electroencephalogram signals were extracted with information detailing their domain (time or frequency), and their spacial scalp and time placement. For both domains, two different classification pipelines were applied in order to select the most relevant features: one to rank and select the top features and another to recursively eliminate the least relevant feature. The Support Vector Machine (linear and non-linear) is the classification model included in the pipeline. Further observations on the selected features by the applied techniques were performed and discussed in relation to the available knowledge about face recognition. The present work provides an experimental study on the electroencephalogram signals acquired from an experiment in which an array of subjects were asked to identify both culprit and distractor being the culprit related to a previously shown crime scene video.A aplicação de técnicas de Interfaces Cérebro-Computador a testemunhas vitais de um crime pode e provavelmente será uma funcionalidade chave no sistema de justiça. Características de sinais provenientes de eletroencefalograma foram extraídas com informações sobre o seu domínio (tempo ou frequência), e a sua localização espacial e temporal. Para ambos os domínios, dois modelos de classificação diferentes foram aplicados com vista a selecionar as características mais relevantes: um para classificar, ordenar e selecionar as características mais importantes e outro para eliminar recursivamente a característica menos relevante. O modelo utilizado para classificação foi o Support Vector Machine (linear e não linear). Outras observações sobre as características selecionadas pelas técnicas aplicadas foram realizadas e discutidas tendo em conta o conhecimento disponível sobre reconhecimento facial. O presente trabalho fornece um estudo experimental sobre os sinais de eletroencefalograma adquiridos numa experiência na qual foi pedido a um grupo de indivíduos para identificar tanto culpado como distrator, sendo que o culpado estava relacionado a um vídeo de cenário de crime mostrado anteriormente.Mestrado em Engenharia de Computadores e Telemátic

Earables: Wearable Computing on the Ears

Kopfhörer haben sich bei Verbrauchern durchgesetzt, da sie private Audiokanäle anbieten, zum Beispiel zum Hören von Musik, zum Anschauen der neuesten Filme während dem Pendeln oder zum freihändigen Telefonieren. Dank diesem eindeutigen primären Einsatzzweck haben sich Kopfhörer im Vergleich zu anderen Wearables, wie zum Beispiel Smartglasses, bereits stärker durchgesetzt. In den letzten Jahren hat sich eine neue Klasse von Wearables herausgebildet, die als "Earables" bezeichnet werden. Diese Geräte sind so konzipiert, dass sie in oder um die Ohren getragen werden können. Sie enthalten verschiedene Sensoren, um die Funktionalität von Kopfhörern zu erweitern. Die räumliche Nähe von Earables zu wichtigen anatomischen Strukturen des menschlichen Körpers bietet eine ausgezeichnete Plattform für die Erfassung einer Vielzahl von Eigenschaften, Prozessen und Aktivitäten. Auch wenn im Bereich der Earables-Forschung bereits einige Fortschritte erzielt wurden, wird deren Potenzial aktuell nicht vollständig abgeschöpft. Ziel dieser Dissertation ist es daher, neue Einblicke in die Möglichkeiten von Earables zu geben, indem fortschrittliche Sensorikansätze erforscht werden, welche die Erkennung von bisher unzugänglichen Phänomenen ermöglichen. Durch die Einführung von neuartiger Hardware und Algorithmik zielt diese Dissertation darauf ab, die Grenzen des Erreichbaren im Bereich Earables zu verschieben und diese letztlich als vielseitige Sensorplattform zur Erweiterung menschlicher Fähigkeiten zu etablieren. Um eine fundierte Grundlage für die Dissertation zu schaffen, synthetisiert die vorliegende Arbeit den Stand der Technik im Bereich der ohr-basierten Sensorik und stellt eine einzigartig umfassende Taxonomie auf der Basis von 271 relevanten Publikationen vor. Durch die Verbindung von Low-Level-Sensor-Prinzipien mit Higher-Level-Phänomenen werden in der Dissertation anschließ-end Arbeiten aus verschiedenen Bereichen zusammengefasst, darunter (i) physiologische Überwachung und Gesundheit, (ii) Bewegung und Aktivität, (iii) Interaktion und (iv) Authentifizierung und Identifizierung. Diese Dissertation baut auf der bestehenden Forschung im Bereich der physiologischen Überwachung und Gesundheit mit Hilfe von Earables auf und stellt fortschrittliche Algorithmen, statistische Auswertungen und empirische Studien vor, um die Machbarkeit der Messung der Atemfrequenz und der Erkennung von Episoden erhöhter Hustenfrequenz durch den Einsatz von In-Ear-Beschleunigungsmessern und Gyroskopen zu demonstrieren. Diese neuartigen Sensorfunktionen unterstreichen das Potenzial von Earables, einen gesünderen Lebensstil zu fördern und eine proaktive Gesundheitsversorgung zu ermöglichen. Darüber hinaus wird in dieser Dissertation ein innovativer Eye-Tracking-Ansatz namens "earEOG" vorgestellt, welcher Aktivitätserkennung erleichtern soll. Durch die systematische Auswertung von Elektrodenpotentialen, die um die Ohren herum mittels eines modifizierten Kopfhörers gemessen werden, eröffnet diese Dissertation einen neuen Weg zur Messung der Blickrichtung. Dabei ist das Verfahren weniger aufdringlich und komfortabler als bisherige Ansätze. Darüber hinaus wird ein Regressionsmodell eingeführt, um absolute Änderungen des Blickwinkels auf der Grundlage von earEOG vorherzusagen. Diese Entwicklung eröffnet neue Möglichkeiten für Forschung, welche sich nahtlos in das tägliche Leben integrieren lässt und tiefere Einblicke in das menschliche Verhalten ermöglicht. Weiterhin zeigt diese Arbeit, wie sich die einzigarte Bauform von Earables mit Sensorik kombinieren lässt, um neuartige Phänomene zu erkennen. Um die Interaktionsmöglichkeiten von Earables zu verbessern, wird in dieser Dissertation eine diskrete Eingabetechnik namens "EarRumble" vorgestellt, die auf der freiwilligen Kontrolle des Tensor Tympani Muskels im Mittelohr beruht. Die Dissertation bietet Einblicke in die Verbreitung, die Benutzerfreundlichkeit und den Komfort von EarRumble, zusammen mit praktischen Anwendungen in zwei realen Szenarien. Der EarRumble-Ansatz erweitert das Ohr von einem rein rezeptiven Organ zu einem Organ, das nicht nur Signale empfangen, sondern auch Ausgangssignale erzeugen kann. Im Wesentlichen wird das Ohr als zusätzliches interaktives Medium eingesetzt, welches eine freihändige und augenfreie Kommunikation zwischen Mensch und Maschine ermöglicht. EarRumble stellt eine Interaktionstechnik vor, die von den Nutzern als "magisch und fast telepathisch" beschrieben wird, und zeigt ein erhebliches ungenutztes Potenzial im Bereich der Earables auf. Aufbauend auf den vorhergehenden Ergebnissen der verschiedenen Anwendungsbereiche und Forschungserkenntnisse mündet die Dissertation in einer offenen Hard- und Software-Plattform für Earables namens "OpenEarable". OpenEarable umfasst eine Reihe fortschrittlicher Sensorfunktionen, die für verschiedene ohrbasierte Forschungsanwendungen geeignet sind, und ist gleichzeitig einfach herzustellen. Hierdurch werden die Einstiegshürden in die ohrbasierte Sensorforschung gesenkt und OpenEarable trägt somit dazu bei, das gesamte Potenzial von Earables auszuschöpfen. Darüber hinaus trägt die Dissertation grundlegenden Designrichtlinien und Referenzarchitekturen für Earables bei. Durch diese Forschung schließt die Dissertation die Lücke zwischen der Grundlagenforschung zu ohrbasierten Sensoren und deren praktischem Einsatz in realen Szenarien. Zusammenfassend liefert die Dissertation neue Nutzungsszenarien, Algorithmen, Hardware-Prototypen, statistische Auswertungen, empirische Studien und Designrichtlinien, um das Feld des Earable Computing voranzutreiben. Darüber hinaus erweitert diese Dissertation den traditionellen Anwendungsbereich von Kopfhörern, indem sie die auf Audio fokussierten Geräte zu einer Plattform erweitert, welche eine Vielzahl fortschrittlicher Sensorfähigkeiten bietet, um Eigenschaften, Prozesse und Aktivitäten zu erfassen. Diese Neuausrichtung ermöglicht es Earables sich als bedeutende Wearable Kategorie zu etablieren, und die Vision von Earables als eine vielseitige Sensorenplattform zur Erweiterung der menschlichen Fähigkeiten wird somit zunehmend realer

Leveraging EEG-based speech imagery brain-computer interfaces

Speech Imagery Brain-Computer Interfaces (BCIs) provide an intuitive and flexible way of interaction via brain activity recorded during imagined speech. Imagined speech can be decoded in form of syllables or words and captured even with non-invasive measurement methods as for example the Electroencephalography (EEG). Over the last decade, research in this field has made tremendous progress and prototypical implementations of EEG-based Speech Imagery BCIs are numerous. However, most work is still conducted in controlled laboratory environments with offline classification and does not find its way to real online scenarios. Within this thesis we identify three main reasons for these circumstances, namely, the mentally and physically exhausting training procedures, insufficient classification accuracies and cumbersome EEG setups with usually high-resolution headsets. We furthermore elaborate on possible solutions to overcome the aforementioned problems and present and evaluate new methods in each of the domains. In detail we introduce two new training concepts for imagined speech BCIs, one based on EEG activity during silently reading and the other recorded during overtly speaking certain words. Insufficient classification accuracies are addressed by introducing the concept of a Semantic Speech Imagery BCI, which classifies the semantic category of an imagined word prior to the word itself to increase the performance of the system. Finally, we investigate on different techniques for electrode reduction in Speech Imagery BCIs and aim at finding a suitable subset of electrodes for EEG-based imagined speech detection, therefore facilitating the cumbersome setups. All of our presented results together with general remarks on experiences and best practice for study setups concerning imagined speech are summarized and supposed to act as guidelines for further research in the field, thereby leveraging Speech Imagery BCIs towards real-world application.Speech Imagery Brain-Computer Interfaces (BCIs) bieten eine intuitive und flexible Möglichkeit der Interaktion mittels Gehirnaktivität, aufgezeichnet während der bloßen Vorstellung von Sprache. Vorgestellte Sprache kann in Form von Silben oder Wörtern auch mit nicht-invasiven Messmethoden wie der Elektroenzephalographie (EEG) gemessen und entschlüsselt werden. In den letzten zehn Jahren hat die Forschung auf diesem Gebiet enorme Fortschritte gemacht, und es gibt zahlreiche prototypische Implementierungen von EEG-basierten Speech Imagery BCIs. Die meisten Arbeiten werden jedoch immer noch in kontrollierten Laborumgebungen mit Offline-Klassifizierung durchgeführt und finden nicht denWeg in reale Online-Szenarien. In dieser Arbeit identifizieren wir drei Hauptgründe für diesen Umstand, nämlich die geistig und körperlich anstrengenden Trainingsverfahren, unzureichende Klassifizierungsgenauigkeiten und umständliche EEG-Setups mit meist hochauflösenden Headsets. Darüber hinaus erarbeiten wir mögliche Lösungen zur Überwindung der oben genannten Probleme und präsentieren und evaluieren neue Methoden für jeden dieser Bereiche. Im Einzelnen stellen wir zwei neue Trainingskonzepte für Speech Imagery BCIs vor, von denen eines auf der Messung von EEG-Aktivität während des stillen Lesens und das andere auf der Aktivität während des Aussprechens bestimmter Wörter basiert. Unzureichende Klassifizierungsgenauigkeiten werden durch die Einführung des Konzepts eines Semantic Speech Imagery BCI angegangen, das die semantische Kategorie eines vorgestellten Wortes vor dem Wort selbst klassifiziert, um die Performance des Systems zu erhöhen. Schließlich untersuchen wir verschiedene Techniken zur Elektrodenreduktion bei Speech Imagery BCIs und zielen darauf ab, eine geeignete Teilmenge von Elektroden für die EEG-basierte Erkennung von vorgestellter Sprache zu finden, um so die umständlichen Setups zu erleichtern. Alle unsere Ergebnisse werden zusammen mit allgemeinen Bemerkungen zu Erfahrungen und Best Practices für Studien-Setups bezüglich vorgestellter Sprache zusammengefasst und sollen als Richtlinien für die weitere Forschung auf diesem Gebiet dienen, um so Speech Imagery BCIs für die Anwendung in der realenWelt zu optimieren

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

Probabilistic graphical models for brain computer interfaces

Brain computer interfaces (BCI) are systems that aim to establish a new communication path for subjects who su er from motor disabilities, allowing interaction with the environment through computer systems. BCIs make use of a diverse group of physiological phenomena recorded using electrodes placed on the scalp (Electroencephalography, EEG) or electrodes placed directly over the brain cortex (Electrocorticography, ECoG). One commonly used phenomenon is the activity observed in specific areas of the brain in response to external events, called Event Related Potentials (ERP). Among those, a type of response called P300 is the most used phenomenon. The P300 has found application in spellers that make use of the brain's response to the presentation of a sequence of visual stimuli. Another commonly used phenomenon is the synchronization or desynchronization of brain rhythms during the execution or imagination of a motor task, which can be used to differentiate between two or more subject intentions. In the most basic scenario, a BCI system calculates the differences in the power of the EEG rhythms during execution of different tasks. Based on those differences, the BCI decides which task has been executed (e.g., motor imagination of left or right hand). Current approaches are mainly based on machine learning techniques that learn the distribution of the power values of the brain signals for each of the possible classes. In this thesis, making use of EEG and ECoG recording methods, we propose the use of probabilistic graphical models for brain computer interfaces. In the case of ERPs, in particular P300-based spellers, we propose the incorporation of language models at the level of words to increase significantly the performance of the spelling system. The proposed framework allows also the incorporation of different methods that take into account language models based on n-grams, all of this in an integrated structure whose parameters can be efficiently learned. In the context of execution or imagination of motor tasks, we propose techniques that take into account the temporal structure of the signals. Stochastic processes that model temporal dynamics of the brain signals in different frequency bands such as non-parametric Bayesian hidden Markov models are proposed in order to solve the problem of selection of the number of brain states during the execution of motor tasks as well as the selection of the number of components used to model the distribution of the brain signals. Following up on the same line of thought, hidden conditional random fields are proposed for classification of synchronous motor tasks. The combination of hidden states with the discriminative power of conditional random fields is shown to increase the classification performance of imaginary motor movements. In the context of asynchronous BCIs, we propose a method based on latent dynamic conditional random fields that is capable of modeling the internal temporal dynamics related to the generation of the brain signals, and external brain dynamics related to the execution of different mental tasks. Finally, in the context of asynchronous BCIs a model based on discriminative graphical models is presented for continuous classification of finger movements from ECoG data. We show that the incorporation of temporal dynamics of the brain signals in the classification stages increases significantly the classification accuracy of different mental states which can lead to a more effective interaction between the subject and the environment