Dynamic fluctuations in ascending heart-to-brain communication under mental stress

Dynamical information exchange between central and autonomic nervous systems, as referred to functional brain-heart interplay, occurs during emotional and physical arousal. It is well documented that physical and mental stress lead to sympathetic activation. Nevertheless, the role of autonomic inputs in nervous system-wise communication under mental stress is yet unknown. In this study, we estimated the causal and bidirectional neural modulations between electroencephalogram (EEG) oscillations and peripheral sympathetic and parasympathetic activities using a recently proposed computational framework for a functional brain-heart interplay assessment, namely the sympathovagal synthetic data generation model. Mental stress was elicited in 37 healthy volunteers by increasing their cognitive demands throughout three tasks associated with increased stress levels. Stress elicitation induced an increased variability in sympathovagal markers, as well as increased variability in the directional brain-heart interplay. The observed heart-to-brain interplay was primarily from sympathetic activity targeting a wide range of EEG oscillations, whereas variability in the efferent direction seemed mainly related to EEG oscillations in the c band. These findings extend current knowledge on stress physiology, which mainly referred to top-down neural dynamics. Our results suggest that mental stress may not cause an increase in sympathetic activity exclusively as it initiates a dynamic fluctuation within brain-body networks including bidirectional interactions at a brain-heart level. We conclude that directional brain-heart interplay measurements may provide suitable biomarkers for a quantitative stress assessment and bodily feedback may modulate the perceived stress caused by increased cognitive demand

Affect Recognition Using Electroencephalography Features

Affect is the psychological display of emotion often described with three principal dimensions: 1) valence 2) arousal and 3) dominance. This thesis work explores the ability of computers to recognize human emotions using Electroencephalography (EEG) features. The development of computer systems to classify human emotions using physiological signals has recently gained pace in the research and technological community. This is because by using EEG to analyze the cognitive state one will be able to establish a direct communication channel between a computer and the human brain. Other applications of recognizing the affective states from EEG include identifying stress and cognitive workload on individuals and assist them in relaxation. This thesis is an extensive study on the design of paradigms that help computer systems recognize emotional states given a multichannel Electroencephalogram (EEG) segment. The process of first extracting features from the EEG signals using signal processing and then constructing a predictive model via machine learning is often referred to as paradigms. In this work, we will first present a brief review of the state-of-the-art paradigms that have contributed to the topic of emotional affect recognition. Then the proposed paradigms to recognize the principal dimensions of affect are detailed. Feature selection is also performed in order to select the relevant features. The evaluation of the models created to predict the affective states will be performed quantitatively by calculating the generalization accuracy and qualitatively by interpreting them

A mutual information based adaptive windowing of informative EEG for emotion recognition

Emotion recognition using brain wave signals involves using high dimensional electroencephalogram (EEG) data. In this paper, a window selection method based on mutual information is introduced to select an appropriate signal window to reduce the length of the signals. The motivation of the windowing method comes from EEG emotion recognition being computationally costly and the data having low signal-to-noise ratio. The aim of the windowing method is to find a reduced signal where the emotions are strongest. In this paper, it is suggested, that using only the signal section which best describes emotions improves the classification of emotions. This is achieved by iteratively comparing different-length EEG signals at different time locations using the mutual information between the reduced signal and emotion labels as criterion. The reduced signal with the highest mutual information is used for extracting the features for emotion classification. In addition, a viable framework for emotion recognition is introduced. Experimental results on publicly available datasets, DEAP and MAHNOB-HCI, show significant improvement in emotion recognition accuracy

Addressing the challenges posed by human machine interfaces based on force sensitive resistors for powered prostheses

Despite the advancements in the mechatronics aspect of prosthetic devices, prostheses control still lacks an interface that satisfies the needs of the majority of users. The research community has put great effort into the advancements of prostheses control techniques to address users’ needs. However, most of these efforts are focused on the development and assessment of technologies in the controlled environments of laboratories. Such findings do not fully transfer to the daily application of prosthetic systems. The objectives of this thesis focus on factors that affect the use of Force Myography (FMG) controlled prostheses in practical scenarios. The first objective of this thesis assessed the use of FMG as an alternative or synergist Human Machine Interface (HMI) to the more traditional HMI, i.e. surface Electromyography (sEMG). The assessment for this study was conducted in conditions that are relatively close to the real use case of prosthetic applications. The HMI was embedded in the custom prosthetic prototype that was developed for the pilot participant of the study using an off-the-shelf prosthetic end effector. Moreover, prostheses control was assessed as the user moved their limb in a dynamic protocol.The results of the aforementioned study motivated the second objective of this thesis: to investigate the possibility of reducing the complexity of high density FMG systems without sacrificing classification accuracies. This was achieved through a design method that uses a high density FMG apparatus and feature selection to determine the number and location of sensors that can be eliminated without significantly sacrificing the system’s performance. The third objective of this thesis investigated two of the factors that contribute to increased errors in force sensitive resistor (FSR) signals used in FMG controlled prostheses: bending of force sensors and variations in the volume of the residual limb. Two studies were conducted that proposed solutions to mitigate the negative impact of these factors. The incorporation of these solutions into prosthetic devices is discussed in these studies.It was demonstrated that FMG is a promising HMI for prostheses control. The facilitation of pattern recognition with FMG showed potential for intuitive prosthetic control. Moreover, a method for the design of a system that can determine the required number of sensors and their locations on each individual to achieve a simpler system with comparable performance to high density FMG systems was proposed and tested. The effects of the two factors considered in the third objective were determined. It was also demonstrated that the proposed solutions in the studies conducted for this objective can be used to increase the accuracy of signals that are commonly used in FMG controlled prostheses

A Feature Selection Method for Driver Stress Detection Using Heart Rate Variability and Breathing Rate

Driver stress is a major cause of car accidents and death worldwide. Furthermore, persistent stress is a health problem, contributing to hypertension and other diseases of the cardiovascular system. Stress has a measurable impact on heart and breathing rates and stress levels can be inferred from such measurements. Galvanic skin response is a common test to measure the perspiration caused by both physiological and psychological stress, as well as extreme emotions. In this paper, galvanic skin response is used to estimate the ground truth stress levels. A feature selection technique based on the minimal redundancy-maximal relevance method is then applied to multiple heart rate variability and breathing rate metrics to identify a novel and optimal combination for use in detecting stress. The support vector machine algorithm with a radial basis function kernel was used along with these features to reliably predict stress. The proposed method has achieved a high level of accuracy on the target dataset.Comment: In Proceedings of the 15th International Conference on Machine Vision (ICMV), Rome, Italy, 18-20 November 2022. arXiv admin note: text overlap with arXiv:2206.0322

An Empirical Comparative Study on the Two Methods of Eliciting Singers’ Emotions in Singing: Self-Imagination and VR Training

Emotional singing can affect vocal performance and the audience’s engagement. Chinese universities use traditional training techniques for teaching theoretical and applied knowledge. Self-imagination is the predominant training method for emotional singing. Recently, virtual reality (VR) technologies have been applied in several fields for training purposes. In this empirical comparative study, a VR training task was implemented to elicit emotions from singers and further assist them with improving their emotional singing performance. The VR training method was compared against the traditional self-imagination method. By conducting a two-stage experiment, the two methods were compared in terms of emotions’ elicitation and emotional singing performance. In the first stage, electroencephalographic (EEG) data were collected from the subjects. In the second stage, self-rating reports and third-party teachers’ evaluations were collected. The EEG data were analyzed by adopting the max-relevance and min-redundancy algorithm for feature selection and the support vector machine (SVM) for emotion recognition. Based on the results of EEG emotion classification and subjective scale, VR can better elicit the positive, neutral, and negative emotional states from the singers than not using this technology (i.e., self-imagination). Furthermore, due to the improvement of emotional activation, VR brings the improvement of singing performance. The VR hence appears to be an effective approach that may improve and complement the available vocal music teaching methods

EMOTION RECOGNITION BASED ON VARIOUS PHYSIOLOGICAL SIGNALS - A REVIEW

Emotion recognition is one of the biggest challenges in human-human and human-computer interaction. There are various approaches to recognize emotions like facial expression, audio signals, body poses, and gestures etc. Physiological signals play vital role in emotion recognition as they are not controllable and are of immediate response type. In this paper, we discuss the research done on emotion recognition using skin conductance, skin temperature, electrocardiogram (ECG), electromyography (EMG), and electroencephalogram (EEG) signals. Altogether, the same methodology has been adopted for emotion recognition techniques based upon various physiological signals. After survey, it is understood that none of these methods are fully efficient standalone but the efficiency can be improved by using combination of physiological signals. The study of this paper provides an insight on the current state of research and challenges faced during emotion recognition using physiological signals, so that research can be advanced for better recognition

A Review on EEG Signals Based Emotion Recognition

Emotion recognition has become a very controversial issue in brain-computer interfaces (BCIs). Moreover, numerous studies have been conducted in order to recognize emotions. Also, there are several important definitions and theories about human emotions. In this paper we try to cover important topics related to the field of emotion recognition. We review several studies which are based on analyzing electroencephalogram (EEG) signals as a biological marker in emotion changes. Considering low cost, good time and spatial resolution, EEG has become very common and is widely used in most BCI applications and studies. First, we state some theories and basic definitions related to emotions. Then some important steps of an emotion recognition system like different kinds of biologic measurements (EEG, electrocardiogram [EEG], respiration rate, etc), offline vs online recognition methods, emotion stimulation types and common emotion models are described. Finally, the recent and most important studies are reviewed

Classification of EEG and fNIRS signals from Completely Locked-in State Patients for a Brain-Computer Interface communication system

People suffering from complete motor paralysis with no severe deficiency in cognitive abilities, syndrome called Completely Locked in State (CLIS), remain aware of their surroundings without being able to interact and communicate in any way. In this context, the only possibility of communicating is by the techniques of Brain-Computer Interface. In this work, the focus is on the features extraction and selection on EEG and fNIRS signals and, finally, on the combination of the two to develop a system capable of classifying affirmative and negative answers from users in CLIS. The analysis considers the data collected in 4 visits to one patient. The choice to focus on a single case was made because the psychophysical considerations on the state of the patient are fundamental interpreting the results and the author of this work had the opportunity to participate directly in some acquisition. Offline analysis led to good results in the classification of fNIRS signals. Once again, using EEG signals it was not possible to successfully classify yes/no answers. Finally, the combination of EEG and fNIRS features did not improve the performance of the system.ope