Local properties of vigilance states: EMD analysis of EEG signals during sleep-waking states of freely moving rats

Understanding the inherent dynamics of the EEG associated to sleep-waking can provide insights into its basic neural regulation. By characterizing the local properties of the EEG using power spectrum, empirical mode decomposition (EMD) and Hilbert-spectral analysis, we can examine the dynamics over a range of time-scales. We analyzed rat EEG during wake, NREMS and REMS using these methods. The average instantaneous phase, power spectral density (PSD) of intrinsic mode functions (IMFs) and the energy content in various frequency bands show characteristic changes in each of the vigilance states. The 2nd and 7th IMFs show changes in PSD for wake and REMS, suggesting that those modes may carry wake- and REMS-associated cognitive, conscious and behavior-specific information of an individual even though the EEG may appear similar. The energy content in θ2 (6Hz-9Hz) band of the 1st IMF for REMS is larger than that of wake. The decrease in the phase function of IMFs from wake to REMS to NREMS indicates decrease of the mean frequency in these states, respectively. The rate of information processing in waking state is more in the time scale described by the first three IMFs than in REMS state. However, for IMF5-IMF7, the rate is more for REMS than that for wake. We obtained Hilbert-Huang spectral entropy, which is a suitable measure of information processing in each of these state-specific EEG. It is possible to evaluate the complex dynamics of the EEG in each of the vigilance states by applying measures based on EMD and Hilbert-transform. Our results suggest that the EMD based nonlinear measures of the EEG can provide useful estimates of the information possessed by various oscillations associated with the vigilance states. Further, the EMD-based spectral measures may have implications in understanding anatamo-physiological correlates of sleep-waking behavior and clinical diagnosis of sleep-pathology

Tracking brain dynamics across transitions of consciousness

How do we lose and regain consciousness? The space between healthy wakefulness and unconsciousness encompasses a series of gradual and rapid changes in brain activity. In this thesis, I investigate computational measures applicable to the electroencephalogram to quantify the loss and recovery of consciousness from the perspective of modern theoretical frameworks. I examine three different transitions of consciousness caused by natural, pharmacological and pathological factors: sleep, sedation and coma. First, I investigate the neural dynamics of falling asleep. By combining the established methods of phase-lag brain connectivity and EEG microstates in a group of healthy subjects, a unique microstate is identified, whose increased duration predicts behavioural unresponsiveness to auditory stimuli during drowsiness. This microstate also uniquely captures an increase in frontoparietal theta connectivity, a putative marker of the loss of consciousness prior to sleep onset. I next examine the loss of behavioural responsiveness in healthy subjects undergoing mild and moderate sedation. The Lempel-Ziv compression algorithm is employed to compute signal complexity and symbolic mutual information to assess information integration. An intriguing dissociation between responsiveness and drug level in blood during sedation is revealed: responsiveness is best predicted by the temporal complexity of the signal at single- channel and low-frequency integration, whereas drug level is best predicted by the complexity of spatial patterns and high-frequency integration. Finally, I investigate brain connectivity in the overnight EEG recordings of a group of patients in acute coma. Graph theory is applied on alpha, theta and delta networks to find that increased variability in delta network integration early after injury predicts the eventual coma recovery score. A case study is also described where the re-emergence of frontoparietal connectivity predicted a full recovery long before behavioural improvement. The findings of this thesis inform prospective clinical applications for tracking states of consciousness and advance our understanding of the slow and fast brain dynamics underlying its transitions. Collating these findings under a common theoretical framework, I argue that the diversity of dynamical states, in particular in temporal domain, and information integration across brain networks are fundamental in sustaining consciousness.My PhD was funded by the Cambridge Trust and a MariaMarina award from Lucy Cavendish College

General Anesthesia as a Multimodal Individualized Clinical Concept

In this book, a series of modern multimodal monitoring techniques during general anesthesia are presented, with a focus on patient-oriented anesthesia based on the individual needs of each patient reflected in the degree of hypnosis, the nociception–antinociception balance, and neuromuscular transmission. Moreover, a series of secondary implications for hemodynamic status, post-anesthetic recovery, and patient satisfaction are highlighted

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Electrostimulation Contingencies and Attention, Electrocortical Activity and Neurofeedback

There is a growing body of evidence for diverse ways of modulating neuronal processing to improve cognitive performance. These include brain-based feedback, self-regulation techniques such as EEG-neurofeedback, and stimulation strategies, alone or in combination. The thesis goal was to determine whether a combined strategy would have advantages for normal cognitive function; specifically operant control of EEG activity in combination with transcutaneous electro-acustimulation. In experiment one the association between transcutaneous electroacustimulation (EA) and improved perceptual sensitivity was demonstrated with a visual GO/NOGO attention task (Chen et al, 2011). Furthermore reduced commission errors were related to an electrocortical motor inhibition component during and after alternating high and low frequency EA, whereas habituation in the control group with sham stimulation was related to different independent components. Experiment two applied frequency-domain ICA to detect changes in EEG power spectra from the eyes-closed to the eyes-open state (Chen et al, 2012). A multiple step approach was provided for analysing the spatiotemporal dynamics of default mode and resting state networks of cerebral EEG sources, preferable to conventional scalp EEG data analysis. Five regions were defined, compatible with fMRI studies. In experiment three the EA approach of Exp I was combined with sensorimotor rhythm (SMR) neurofeedback. SMR training improved perceptual sensitivity, an effect not found in a noncontingent feedback group. However, non-significant benefits resulted from EA. With ICA spectral power analysis changes in frontal beta power were associated with contingent SMR training. Possible long-term effects on an attention network in the resting EEG were also found after SMR training, compared with mock SMR training. In conclusion, this thesis has supplied novel evidence for significant cognitive and electrocortical effects of neurofeedback training and transcutaneous electro-acustimulation in healthy humans. Possible implications of these findings and suggestions for future research are considered

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