An eeg based study of unintentional sleep onset

Ph.DDOCTOR OF PHILOSOPH

A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear

Measuring Brain Activation Patterns from Raw Single-Channel EEG during Exergaming: A Pilot Study

Physical and cognitive rehabilitation is deemed crucial to attenuate symptoms and to improve the quality of life in people with neurodegenerative disorders, such as Parkinson's Disease. Among rehabilitation strategies, a novel and popular approach relies on exergaming: the patient performs a motor or cognitive task within an interactive videogame in a virtual environment. These strategies may widely benefit from being tailored to the patient's needs and engagement patterns. In this pilot study, we investigated the ability of a low-cost BCI based on single-channel EEG to measure the user's engagement during an exergame. As a first step, healthy subjects were recruited to assess the system's capability to distinguish between (1) rest and gaming conditions and (2) gaming at different complexity levels, through Machine Learning supervised models. Both EEG and eye-blink features were employed. The results indicate the ability of the exergame to stimulate engagement and the capability of the supervised classification models to distinguish resting stage from game-play(accuracy > 95%). Finally, different clusters of subject responses throughout the game were identified, which could help define models of engagement trends. This result is a starting point in developing an effectively subject-tailored exergaming system

Performance Under Pressure: Examination of Relevant Neurobiological and Genetic Influence

Satisfactory human performance demands the complex interaction of multiple factors such as arousal/motivation, emotion expression and regulation, intricate synchronization of central and peripheral motor processes, all recruited in the service of adaptive, moment to moment decision making. The segregation of these various factors aids in the understanding of their complex interactions. Recently, scientific investigation has focused on understanding the integration of these various factors. The complementary role of emotion and cognition in successful human performance is emphasized. As a viable metric of emotion regulation differences in asymmetry of human brain frontal activity have traditionally been utilized to index certain trait predispositions within the approach/withdrawal dimension of emotion/motivation. Researchers have begun to make a case for an acute or state difference in frontal asymmetry. This "Capability Model" posits the neural underpinnings of the relative difference in electrical activity between the left and right frontal lobes as a phasic/situational mechanism possibly sub-serving the integration of emotion and cognition during challenge. The current study demonstrates support for this situational/state model of frontal asymmetry. Thirty channels of EEG were collected along with, skin conductance, heart rate and acoustic startle amplitudes while subjects were engaged in two levels of a working memory task under three increasing levels of stress (final level=electric stimuli/shock). Hierarchical regression results implicate state frontal asymmetry differences as having a mediating role in the adaptive regulation of emotion during enhanced performance on an N-back working memory task but only in the high stress condition. During shock /threat of shock participants with higher state asymmetry scores showed significant attenuation of eye-blink startle magnitudes, faster reaction times and increased accuracy. This suggests an integration of emotion and cognition

#Scanners: exploring the control of adaptive films using brain-computer interaction

This paper explores the design space of bio-responsive entertainment, in this case using a film that responds to the brain and blink data of users. A film was created with four parallel channels of footage, where blinking and levels of attention and meditation, as recorded by a commercially available EEG device, affected which footage participants saw. As a performance-led piece of research in the wild, this experience, named #Scanners, was presented at a week long national exhibition in the UK. We examined the experiences of 35 viewers, and found that these forms of partially-involuntary control created engaging and enjoyable, but sometimes distracting, experiences. We translate our findings into a two-dimensional design space between the extent of voluntary control that a physiological measure can provide against the level of conscious awareness that the user has of that control. This highlights that novel design opportunities exist when deviating from these two-dimensions - when giving up conscious control and when abstracting the affect of control. Reflection on of how viewers negotiated this space during an experience reveals novel design tactics

Eye Movement and Pupil Measures: A Review

Our subjective visual experiences involve complex interaction between our eyes, our brain, and the surrounding world. It gives us the sense of sight, color, stereopsis, distance, pattern recognition, motor coordination, and more. The increasing ubiquity of gaze-aware technology brings with it the ability to track gaze and pupil measures with varying degrees of fidelity. With this in mind, a review that considers the various gaze measures becomes increasingly relevant, especially considering our ability to make sense of these signals given different spatio-temporal sampling capacities. In this paper, we selectively review prior work on eye movements and pupil measures. We first describe the main oculomotor events studied in the literature, and their characteristics exploited by different measures. Next, we review various eye movement and pupil measures from prior literature. Finally, we discuss our observations based on applications of these measures, the benefits and practical challenges involving these measures, and our recommendations on future eye-tracking research directions

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others