EEG analysis for understanding stress based on affective model basis function

Coping with stress has shown to be able to avoid many complications in medical condition. In this paper we present an alternative method in analyzing and understanding stress using the four basic emotions of happy, calm, sad and fear as our basis function. Electroencephalogram (EEG) signals were captured from the scalp of the brain and measured in responds to various stimuli from the four basic emotions to stimulating stress base on the IAPS emotion stimuli. Features from the EEG signals were extracted using the Kernel Density Estimation (KDE) and classified using the Multilayer Perceptron (MLP), a neural network classifier to obtain accuracy of the subject’s emotion leading to stress. Results have shown the potential of using the basic emotion basis function to visualize the stress perception as an alternative tool for engineers and psychologist. Keywords: Electroencephalography (EEG), Kernel Density Estimation (KDE), Multi-layer Perceptron (MLP), Valance (V), Arousal (A

A Physiological Computing System to Improve Human-Robot Collaboration by Using Human Comfort Index

Fluent human-robot collaboration requires a robot teammate to understand, learn, and adapt to the human\u27s psycho-physiological state. Such collaborations require a physiological computing system that monitors human biological signals during human-robot collaboration (HRC) to quantitatively estimate a human\u27s level of comfort, which we have termed in this research as comfortability index (CI) and uncomfortability index (UnCI). We proposed a human comfort index estimation system (CIES) that uses biological signals and subjective metrics. Subjective metrics (surprise, anxiety, boredom, calmness, and comfortability) and physiological signals were collected during a human-robot collaboration experiment that varied the robot\u27s behavior. The emotion circumplex model is adapted to calculate the CI from the participant\u27s quantitative data as well as physiological data. This thesis developed a physiological computing system that estimates human comfort levels from physiological by using the circumplex model approach. The data was collected from multiple experiments and machine learning models trained, and their performance was evaluated. As a result, a subject-independent model was tested to determine the robot behavior based on human comfort level. The results from multiple experiments indicate that the proposed CIES model improves human comfort by providing feedback to the robot. In conclusion, physiological signals can be used for personalized robots, and it has the potential to improve safety for humans and increase the fluency of collaboration

Mutual Information in the Frequency Domain for Application in Biological Systems

Biological systems are comprised of multiple components that typically interact nonlinearly and produce multiple outputs (time series/signals) with specific frequency characteristics. Although the exact knowledge of the underlying mechanism remains unknown, the outputs observed from these systems can provide the dependency relations through quantitative methods and increase our understanding of the original systems. The nonlinear relations at specific frequencies require advanced dependency measures to capture the generalized interactions beyond typical correlation in the time domain or coherence in the frequency domain. Mutual information from Information Theory is such a quantity that can measure statistical dependency between random variables. Herein, we develop a model–free methodology for detection of nonlinear relations between time series with respect to frequency, that can quantify dependency under a general probabilistic framework. Classic nonlinear dynamical system and their coupled forms (Lorenz, bidirectionally coupled Lorenz, and unidirectionally coupled Macky–Glass systems) are employed to generate artificial data and to test the proposed methodology. Comparisons between the performances of this measure and a conventional linear measure are presented from applications to the artificial data. This set of results indicates that the proposed methodology is better in capturing the dependency between the variables of the systems. This measure of dependency is also applied to a real–world electrophysiological dataset for emotion analysis to study brain stimuli–response functional connectivity. The results reveal distinct brain regions and specific frequencies that are involved in emotional processing

EEG affective modelling for dysphoria understanding

Dysphoria is a state of dissatisfaction, restlessness or fidgeting. It is a state of feeling unwell in relation to mental and emotional discomfort. If this state is not carefully handled, it may lead to depression, anxiety, and stress. To date, 21-item instruments of Depression, Anxiety and Stress Scale (DASS) is employed to measure dysphoria. Although DASS provides a quantitative assessment of the human affective state, it is subjected to interpretation. To complicate matters, pre-cursor emotion and pre-emotion of the participants can result in biasness of the DASS report. Hence, a more direct method in measuring human affective state by analyzing the brain pattern is proposed. The approach can also address the dynamic affective state which is needed in detecting dysphoria. Brain waves pattern are collected using the electroencephalogram (EEG) device and used as the input to analyze the underlying emotion. In this paper, relevant features were extracted using Mel-frequency cepstral coefficients (MFCC) and classified with Multi-Layer Perceptron (MLP). The experimental results show potential of differentiating between positive and negative emotion with comparable accuracy. Subsequently, it is envisaged that the proposed model can be extended as a tool that can be used to measure stress and anxiety in work places and education institutions

EEG based assessment of emotional wellbeing in smart environment

Abstract. Smart technologies are frequently united and automated in our everyday settings and commonplace task by linking computers and other devices. While there has been a necessity to build smart environments for an easy and comfortable life, research on measuring wellbeing in this environment becomes increasingly intensive. Emotion is one of the decisive aspects of wellbeing that encourages us to work effectively, manage, and cope with stress, and affect our physical health. This work evaluates the EEG signal to measure individuals the different emotional states in a smart space by creating a computer gaming scenario. EEG, a physiological signal which provides details on mental, physiological, and emotional states, EEG frequency bands are strongly correlated with positive and negative emotional responses. Since brain left frontal cortical area is responsible for positive emotion and the right frontal region associate, therefore, we choose two pairs of EEG electrodes F3-F4, and F7-F8 to assess the game player emotional states during the gaming situations. We measure the EEG frontal alpha asymmetry (FAA) by comparing variations in the alpha band power levels in the left and right frontal cortex, corresponding to positive and negative emotions. Our experiment outcome reveals considerable support with the emotional variance of the test participants. We note that multiple interruptions during the gaming situation create irritation to the test subjects. These findings also confirm that F3 and F4 EEG channels are the most sensitive to human emotional responses compared to F7 and F8 channels

On automatic emotion classification using acoustic features

In this thesis, we describe extensive experiments on the classification of emotions from speech using acoustic features. This area of research has important applications in human computer interaction. We have thoroughly reviewed the current literature and present our results on some of the contemporary emotional speech databases. The principal focus is on creating a large set of acoustic features, descriptive of different emotional states and finding methods for selecting a subset of best performing features by using feature selection methods. In this thesis we have looked at several traditional feature selection methods and propose a novel scheme which employs a preferential Borda voting strategy for ranking features. The comparative results show that our proposed scheme can strike a balance between accurate but computationally intensive wrapper methods and less accurate but computationally less intensive filter methods for feature selection. By using the selected features, several schemes for extending the binary classifiers to multiclass classification are tested. Some of these classifiers form serial combinations of binary classifiers while others use a hierarchical structure to perform this task. We describe a new hierarchical classification scheme, which we call Data-Driven Dimensional Emotion Classification (3DEC), whose decision hierarchy is based on non-metric multidimensional scaling (NMDS) of the data. This method of creating a hierarchical structure for the classification of emotion classes gives significant improvements over other methods tested. The NMDS representation of emotional speech data can be interpreted in terms of the well-known valence-arousal model of emotion. We find that this model does not givea particularly good fit to the data: although the arousal dimension can be identified easily, valence is not well represented in the transformed data. From the recognitionresults on these two dimensions, we conclude that valence and arousal dimensions are not orthogonal to each other. In the last part of this thesis, we deal with the very difficult but important topic of improving the generalisation capabilities of speech emotion recognition (SER) systems over different speakers and recording environments. This topic has been generally overlooked in the current research in this area. First we try the traditional methods used in automatic speech recognition (ASR) systems for improving the generalisation of SER in intra– and inter–database emotion classification. These traditional methods do improve the average accuracy of the emotion classifier. In this thesis, we identify these differences in the training and test data, due to speakers and acoustic environments, as a covariate shift. This shift is minimised by using importance weighting algorithms from the emerging field of transfer learning to guide the learning algorithm towards that training data which gives better representation of testing data. Our results show that importance weighting algorithms can be used to minimise the differences between the training and testing data. We also test the effectiveness of importance weighting algorithms on inter–database and cross-lingual emotion recognition. From these results, we draw conclusions about the universal nature of emotions across different languages

MULTIVARIATE MODELING OF COGNITIVE PERFORMANCE AND CATEGORICAL PERCEPTION FROM NEUROIMAGING DATA

State-of-the-art cognitive-neuroscience mainly uses hypothesis-driven statistical testing to characterize and model neural disorders and diseases. While such techniques have proven to be powerful in understanding diseases and disorders, they are inadequate in explaining causal relationships as well as individuality and variations. In this study, we proposed multivariate data-driven approaches for predictive modeling of cognitive events and disorders. We developed network descriptions of both structural and functional connectivities that are critical in multivariate modeling of cognitive performance (i.e., fluency, attention, and working memory) and categorical perceptions (i.e., emotion, speech perception). We also performed dynamic network analysis on brain connectivity measures to determine the role of different functional areas in relation to categorical perceptions and cognitive events. Our empirical studies of structural connectivity were performed using Diffusion Tensor Imaging (DTI). The main objective was to discover the role of structural connectivity in selecting clinically interpretable features that are consistent over a large range of model parameters in classifying cognitive performances in relation to Acute Lymphoblastic Leukemia (ALL). The proposed approach substantially improved accuracy (13% - 26%) over existing models and also selected a relevant, small subset of features that were verified by domain experts. In summary, the proposed approach produced interpretable models with better generalization.Functional connectivity is related to similar patterns of activation in different brain regions regardless of the apparent physical connectedness of the regions. The proposed data-driven approach to the source localized electroencephalogram (EEG) data includes an array of tools such as graph mining, feature selection, and multivariate analysis to determine the functional connectivity in categorical perceptions. We used the network description to correctly classify listeners behavioral responses with an accuracy over 92% on 35 participants. State-of-the-art network description of human brain assumes static connectivities. However, brain networks in relation to perception and cognition are complex and dynamic. Analysis of transient functional networks with spatiotemporal variations to understand cognitive functions remains challenging. One of the critical missing links is the lack of sophisticated methodologies in understanding dynamics neural activity patterns. We proposed a clustering-based complex dynamic network analysis on source localized EEG data to understand the commonality and differences in gender-specific emotion processing. Besides, we also adopted Bayesian nonparametric framework for segmentation neural activity with a finite number of microstates. This approach enabled us to find the default network and transient pattern of the underlying neural mechanism in relation to categorical perception. In summary, multivariate and dynamic network analysis methods developed in this dissertation to analyze structural and functional connectivities will have a far-reaching impact on computational neuroscience to identify meaningful changes in spatiotemporal brain activities

Review of EEG-based pattern classification frameworks for dyslexia

Dyslexia is a disability that causes difficulties in reading and writing despite average intelligence. This hidden disability often goes undetected since dyslexics are normal and healthy in every other way. Electroencephalography (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. The aims of this paper are to examine pros and cons of existing EEG-based pattern classification frameworks for dyslexia and recommend optimisations through the findings to assist future research. A critical analysis of the literature is conducted focusing on each framework’s (1) data collection, (2) pre-processing, (3) analysis and (4) classification methods. A wide range of inputs as well as classification approaches has been experimented for the improvement in EEG-based pattern classification frameworks. It was uncovered that incorporating reading- and writing-related tasks to experiments used in data collection may help improve these frameworks instead of using only simple tasks, and those unwanted artefacts caused by body movements in the EEG signals during reading and writing activities could be minimised using artefact subspace reconstruction. Further, support vector machine is identified as a promising classifier to be used in EEG-based pattern classification frameworks for dyslexia

Lidské vnímání v situaci nejistoty: Vizuální, auditivní a vtělené reakce na nejednoznačné stimuly.

Naše smysly se vyvinuly tak, abychom z okolního prostředí získávat optimální množství informací. Tato optimalizace ovšem znamená, že je třeba počítat s chybami. Proto, abychom předešli těm s významným dopadem, vyvinula se u člověka tendence k nadhodnocování významu vzájemných souvislostí (i ve smyslu vnímání vzorů a posloupností). Ve své práci jsem testovala schopnost vyhodnocování vizuálních a akustických stimulů. Za použití počítačové grafiky byl vyvinut soubor testovacích stimulů, kde bylo rozložení prvků určeno sofistikovaným generátorem pseudo-náhodných čísel. Tyto výsledné masky s různou mírou průhlednosti byly užity k překrytí geometrických tvarů. Podobného postupu bylo užito k vytvoření černobílých stimulů s vysokým kontrastem. Za použití metod bayesovské statistiky jsem nalezla vzájemnou provázanost schopnosti určit přítomnost vzoru (a její absenci) a stylu myšlení, specificky racionálního a na intuici založeného. Dále jsem pak použila nejednoznačné výrazy tváře a vokalizace vysoce intenzivních afektivních stavů (bolest a slast) a stavů nízké intenzity (neutrální výraz/promluva, úsměv/smích). Výsledkem je zjištění, že vysoká intenzita projevu je spojena s nízkou schopností respondentů správně vyhodnotit valenci vizuálních i akustických stimulů. Díky použitému statistickému přístupu jsem...In order to orient ourselves in the environment our senses have evolved so as to acquire optimal information. The optimization, however, incurs mistakes. To avoid costly ones, the over-perception of patterns (in humans) augments the decision making. I tested the decision- making in two modalities, acoustic and visual. A set of stimuli (using computer-generated graphics, based on output from a very good pseudo random generator) was produced: masks with a random pattern with varying degree of transparency over geometrical figures were used, followed by similar task that involved black and white high-contrast patterns. In both cases, I was able to find, using a Bayesian statistical approach, that the ability to detect the correct pattern presence (or lack thereof) was related to respondents' thinking styles, specifically Rationality and Intuition. Furthermore, I used ambiguous facial expressions, and accompanying vocalizations, of high-intensity affects (pain, pleasure and fear) and low- intensity (neutral and smile/laughter). My findings evidenced that the high-intensity facial expressions and vocalizations were rated with a low probability of correct response. Differences in the consistency of the ratings were detected and also the range of probabilities of being due to chance (guessing). When...Katedra filosofie a dějin přírodních vědDepartment of Philosophy and History of SciencePřírodovědecká fakultaFaculty of Scienc