Effects of theta-frequency binaural beats on post-exercise recovery and stress responsivity

Binaural beats are an auditory illusion perceived when two or more pure tones of similar frequencies are presented dichotically through stereo headphones. This phenomenon is thought to have the potential to facilitate changes in arousal. The present study investigated the effects of 7 Hz binaural beating on post-exercise recovery and stress responsivity in college-aged students (n = 21; 18-29 years old).Theta binaural beats failed to outperform placebo in altering post-exercise recovery or stress responsivity. However, after listening to binaural beats, participants reported feeling more relaxed (6.4% change) and less stressed (11.5% change). Findings from the present study suggest that listening to binaural beats may have subtle psychological effects

Sound Effects on Physiological State and Behavior of Drivers in a Highway Tunnel

Driving behavior in a highway tunnel could be affected by external environmental factors like light, traffic flow, and acoustic environments, significantly when these factors suddenly change at the moment before and after entering a tunnel. It will cause tremendous physiological pressure on drivers because of the reduction of information and the narrow environment. The risks in driving behavior will increase, making drivers more vulnerable than driving on the regular highways. This research focuses on the usually neglected acoustic environment and its effect on drivers' physiological state and driving behavior. Based on the SIMLAB driving simulation platform of a highway tunnel, 45 drivers participated in the experiment. Five different sound scenarios were tested: original highway tunnel sound and a mix of it with four other sounds (slow music, fast music, voice prompt, and siren, respectively). The subjects' physiological state and driving behavior data were collected through heart rate variability (HRV) and electroencephalography (EEG). Also, vehicle operational data, including vehicle speed, steering wheel angle, brake pedal depth, and accelerator pedal depth, were collected. The results indicated that different sound scenarios in the highway tunnel showed significant differences in vehicle speed (p = 0.000, η2 = 0.167) and steering wheel angle (p = 0.007, η2 = 0.126). At the same time, they had no significant difference in HRV and EEG indicators. According to the results, slow music was the best kind of sound related to driving comfort, while the siren sound produced the strongest driver reaction in terms of mental alertness and stress level. The voice-prompt sound most likely caused driver fatigue and overload, but it was the most effective sound affecting safety. The subjective opinion of the drivers indicated that the best sound scenario for the overall experience was slow music (63%), followed by fast music (21%), original highway tunnel sound environment (13%), and voice-prompt sound (3%). The findings of this study will be valuable in improving acoustic environment quality and driving safety in highway tunnels

An Exploration into the Relationship between Indices of Autonomic Nervous System Health and Wellness

The maintenance and promotion of wellness proves to be vital to health. Over the years, existing literature has de-emphasized the contributions of objective health to the phenomenon of wellness, and has emphasized subjectively measured wellness concepts. However, due to the complexity of wellness and its importance in regard to individual and societal health, it is imperative to examine wellness not only from a subjective basis, but also in conjunction with objective explorations. A uniform index of wellness should be established in order to reduce the ambiguity associated with the concept. Therefore, this paper had two major aims that were addressed in three experiments testing college students’ self-report and physiological responses. Aim 1 was to develop a wellness model useful in a wide array of research domains. This was done through rigorous testing of components of my proposed Oliver Health Factor Wellness. Aim 2 was to establish an objective measure of wellness. This was done by correlating subjective wellness responses to wellness measures with objective physiological activity indicative of health. More specifically, I assessed Autonomic Nervous System (ANS) function as a means to explore the health and wellness status of individuals. In this paper, I addressed these aims and posit that my findings will advance scientific knowledge regarding a more steadfast way to measure wellness from an objective standpoint, as well as, a way to evaluate the efficacy of a given therapy by examination of changes in function/autonomic balance. In addition, my findings suggest a more reliable way to measure wellness, specifically, with its inclusion of ANS parameters. Finally, my findings suggest that Heart Rate Variability, in particular, can be utilized as an objective index of Holistic wellness and Optimum Health

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

Melody Informatics: Computational Approaches to Understanding the Relationships Between Human Affective Reasoning and Music

Music is a powerful and complex medium that allows people to express their emotions, while enhancing focus and creativity. It is a universal medium that can elicit strong emotion in people, regardless of their gender, age or cultural background. Music is all around us, whether it is in the sound of raindrops, birds chirping, or a popular song played as we walk along an aisle in a supermarket. Music can also significantly help us regain focus while doing a number of different tasks. The relationship between music stimuli and humans has been of particular interest due to music's multifaceted effects on human brain and body. While music can have an anticonvulsant effect on people's bodily signals and act as a therapeutic stimulus, it can also have proconvulsant effects such as triggering epileptic seizures. It is also unclear what types of music can help to improve focus while doing other activities. Although studies have recognised the effects of music in human physiology, research has yet to systematically investigate the effects of different genres of music on human emotion, and how they correlate with their subjective and physiological responses. The research set out in this thesis takes a human-centric computational approach to understanding how human affective (emotional) reasoning is influenced by sensory input, particularly music. Several user studies are designed in order to collect human physiological data while they interact with different stimuli. Physiological signals considered are: electrodermal activity (EDA), blood volume pulse (BVP), skin temperature (ST), pupil dilation (PD), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Several computational approaches, including traditional machine learning approaches with a combination of feature selection methods are proposed which can effectively identify patterns from small to medium scale physiological feature sets. A novel data visualisation approach called "Gingerbread Animation" is proposed, which allows physiological signals to be converted into images that are compatible with transfer learning methods. A novel stacked ensemble based deep learning model is also proposed to analyse large-scale physiological datasets. In the beginning of this research, two user studies were designed to collect physiological signals from people interacting with visual stimuli. The computational models showed high efficacy in detecting people's emotional reactions. The results provided motivation to design a third user study, where these visual stimuli were combined with music stimuli. The results from the study showed decline in recognition accuracy comparing to the previous study. These three studies also gave a key insight that people's physiological response provide a stronger indicator of their emotional state, compared with their verbal statements. Based on the outcomes of the first three user studies, three more user studies were carried out to look into people's physiological responses to music stimuli alone. Three different music genres were investigated: classical, instrumental and pop music. Results from the studies showed that human emotion has a strong correlation with different types of music, and these can be computationally identified using their physiological response. Findings from this research could provide motivation to create advanced wearable technologies such as smartwatches or smart headphones that could provide personalised music recommendation based on an individual's physiological state. The computational approaches can be used to distinguish music based on their positive or negative effect on human mental health. The work can enhance existing music therapy techniques and lead to improvements in various medical and affective computing research

Detecting Flow Experiences in Cognitive Tasks - A Neurophysiological Approach

Das Flow-Erlebnis beschreibt einen Zustand vollständiger Aufgabenvertiefung und mühelosen Handelns, der mit Höchstleistungen, persönlichem Wachstum, sowie allgemeinem Wohlbefinden verbunden ist. Für Unternehmen stellen häufigere Flow-Erlebnisse der ArbeitnehmerInnen daher auch eine produktivitäts- und zufriedenheitsfördernde Basis dar. Vor allem da sich aktuell globale Phänomene wie die steigende Nachfrage nach Wissensarbeit und das niedrige Arbeitsengagement zuspitzen, können Unternehmen von einer Förderung von Flow profitieren. Die Unterstützung von Flow stellt allerdings aufgrund der Vielfalt von Arbeitnehmerfertigkeiten, -aufgaben, und -arbeitsplätzen eine komplexe Herausforderung dar. WissensarbeiterInnen stehen dynamischen Aufgaben gegenüber, die diverse Kompetenzen und die Kooperation mit anderen erfordern. Arbeitsplätze werden vielseitiger, indem die Grenzen zwischen ko-präsenten und virtuellen Interaktionen verschwinden. Diese Vielfalt bedeutet, dass eine solide Flow-Förderung nur durch personen-, aufgaben- und situationsunabhängige Ansätze erfolgen kann. Aus diesem Grund werden zunehmend die neurophysiologischen Grundlagen des Flow-Erlebens untersucht. Auf deren Basis könnten adaptive Neuro-Informationssysteme entwickelt werden, die mittels tragbarer Sensorik Flow kontinuierlich erkennen und fördern können. Diese Wissensbasis ist bislang jedoch nur spärlich und in stark fragmentierter Form vorhanden. Für das Individuum existieren lediglich konkurrierende Vorschläge, die noch nicht durch situations- und sensorübergreifende Studien konsolidiert wurden. Für Gruppen existiert noch fast keine Forschung zu neurophysiologischen Flow-Korrelaten, insbesondere keine im Kontext digital-mediierter Interaktionen. In dieser Dissertation werden genau diese Forschungslücken durch die situationsübergreifende Beobachtung von Flow mit tragbaren EKG und EEG Sensoren adressiert. Dabei werden zentrale Grenzen der experimentellen Flow-Forschung berücksichtigt, vor allem die Defizite etablierter Paradigmen zum kontrollierten Hervorrufen von Flow. Indem Erlebnisse in zwei kognitiven Aufgaben und mehreren Manipulationen (von Schwierigkeit, Natürlichkeit, Autonomie und sozialer Interaktion) variiert werden, wird untersucht, wie Flow intensiver hervorgerufen und wie das Erlebnis stabiler über Situationen hinweg beobachtet werden kann. Die Studienergebnisse deuten dabei insgesamt auf ein Flow-Muster von moderater physiologischer Aktivierung und mentaler Arbeitslast, von erhöhter, aufgabenorientierter Aufmerksamkeit und von affektiver Neutralität hin. Vor allem die EEG Daten zeigen ein diagnostisches Potenzial, schwächere von stärkeren Flow-Zuständen unterscheiden zu können, indem optimale und nicht-optimale Aufgabenschwierigkeiten (für Individuen und Gruppen) erkannt werden. Um das Flow-Erleben weiter zu fördern, werden geeignete Wege für zukünftige Forschung abschließend diskutiert

Psychological and psychophysiological effects of auditory and visual stimuli during various modes of exercise

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research programme had three principal objectives. First, to assess the stability of the exercise heart rate-music tempo preference relationship and its relevance to a range of psychological outcomes. Second, to explore the influence of two personal factors (motivational orientation and dominant attentional style) in a naturalistic exercise-to-music setting. Third, to examine means by which to enhance the exercise experience above and below the ventilatory threshold. In Study 1, a mixed-methods approach was employed to capture responses to differing music tempo conditions across a range of exercise intensities. Results in Study 1 did not support a cubic relationship (Karageorghis et al., 2011) but rather a quadratic one, and there was a weak association between the optimal choice of music tempo and positive psychological outcomes. Music conditions reduced the number of associative thoughts by ~10% across all exercise intensities. Study 2 employed questionnaires with a large sample of female participants (n = 417) attending exercise-to-music classes. Results indicate that motivational orientation and attentional style (Association vs. Dissociation) influence responses to an exercise-to-music class. Study 3 examined the effects of external stimuli (music and video) on psychological variables at moderate and high exercise intensities. Findings served to demonstrate that manipulations of attentional focus can have a salient influence on affect and enjoyment even during high-intensity exercise. The contributions of the research programme include providing empirical evidence that attention can be manipulated during high-intensity exercise using theoretically-guided music selections, and music in combination with video footage, which enhance the exercise experience. Further, the research programme advances understanding of how motivational orientation and attentional style influence responses to music during exercise