We introduce a mean-field model for analysing the dynamics of human consciousness. In particular, inspired by the Giulio Tononi's Integrated Information Theory and by the Max Tegmark's representation of consciousness, we study order-disorder phase transitions on Curie-Weiss models generated by processing EEG signals. The latter have been recorded on healthy individuals undergoing deep sedation. Then, we implement a machine learning tool for classifying mental states using, as input, the critical temperatures computed in the Curie-Weiss models. Results show that, by the proposed method, it is possible to discriminate between states of awareness and states of deep sedation. Besides, we identify a state space for representing the path between mental states, whose dimensions correspond to critical temperatures computed over different frequency bands of the EEG signal. Beyond possible theoretical implications in the study of human consciousness, resulting from our model, we deem relevant to emphasise that the proposed method could be exploited for clinical applications.
In the first section, Introduction, we present our experimental design. In the second section, we characterize the grand average occipital and temporal electrical activity correlated with a contrast in access. In the third section, we characterize the grand average occipital and temporal electrical activity correlated with a contrast in phenomenology and conclude characterizing the grand average occipital and temporal electrical activity co-occurring with unconsciousness.
EEG signals are records of nonlinear solitary waves in human brains. The waves have several types (e.g., a, b, g, q, d) in response to different levels of consciousness. They are classified into two groups: Group-1 consists of complex storm-like waves (a, b, and g); Group-2 is composed of simple quasilinear waves (q and d). In order to elucidate the mechanism of EEG wave formation and propagation, this paper extends the Vlasov-Maxwell equations of Plasma Brain Dynamics (PBD) to a set of two-fluid, self-similar, nonlinear solitary wave equations. Numerical simulations are performed for differen…
Simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can be used to non-invasively measure the spatiotemporal dynamics of the human brain. One challenge is dealing with the artifacts that each modality introduces into the other when the two are recorded concurrently, for example the ballistocardiogram (BCG). We conducted a preliminary comparison of three different MR compatible EEG recording systems and assessed their performance in terms of single-trial classification of the EEG when simultaneously collecting fMRI. We found tradeoffs across all…
The amount of power in different frequency bands of the electroencephalogram (EEG) carries information about the behavioral state of a subject. Hence, neurologists treating epileptic patients monitor the temporal evolution of the different bands. We propose a covariance-based method to detect and characterize epileptic seizures operating on the band-filtered EEG signal. The algorithm is unsupervised, and performs a principal component analysis of intra-cranial EEG recordings, detecting transient fluctuations of the power in each frequency band. Its simplicity makes it suitable for online imple…
Objective: To quantify the effect of inhaled 5% carbon-dioxide/95% oxygen on EEG recordings from patients in non-convulsive status epilepticus (NCSE). Methods: Five children of mixed aetiology in NCSE were given high flow of inhaled carbogen (5% carbon dioxide/95% oxygen) using a face mask for maximum 120s. EEG was recorded concurrently in all patients. The effects of inhaled carbogen on patient EEG recordings were investigated using band-power, functional connectivity and graph theory measures. Carbogen effect was quantified by measuring effect size (Cohen's d) between "before", "during" and …
We want to analyse EEG recordings in order to investigate the phonemic categorization at a very early stage of auditory processing. This problem can be modelled by a supervised classification of functional data. Discrimination is explored via a logistic functional linear model, using a wavelet representation of the data. Different procedures are investigated, based on penalized likelihood and principal component reduction or partial least squares reduction.
This study demonstrates the capability of external signal recording into memory and the reproduction of memory trace of this pattern in EEG by direct AC electrical stimulation of rat cerebral cortex. Additionally, we examine shifts of the DC potential level related to these phenomena. We show that in the course of memory trace reproduction, consecutive phases of engram activation and relaxation are registered and accompanied by corresponding negative and positive DC shifts. The observed electrophysiological changes may reflect consecutive activation and inhibition phases of neural ensembles pa…
Quantification of complexity in neurophysiological signals has been studied using different methods, especially those from information or dynamical system theory. These studies revealed the dependence on different states of consciousness, particularly that wakefulness is characterized by larger complexity of brain signals perhaps due to the necessity of the brain to handle varied sensorimotor information. Thus these frameworks are very useful in attempts at quantifying cognitive states. We set out to analyze different types of signals including scalp and intracerebral electroencephalography (E…
Locked-in Syndrome patients are often misdiagnosed and face pessimistic prognosis because of similarities with disorders of consciousness, a lack of objective biomarkers and a difficult-to-recognize pathogenesis. Biomarkers show promise in identifying similar conditions, utilizing electroencephalography (EEG) data. This data, particularly in the form of event-related potentials (ERPs), while successful in varying applications, suffers from methodological constraints and interpretation obstacles. The study documented in this body of work explores a machine learning paradigm with regards to N400…
Consciousness is supported by complex patterns of brain activity which are indicative of irreversible non-equilibrium dynamics. While the framework of stochastic thermodynamics has facilitated the understanding of physical systems of this kind, its application to infer the level of consciousness from empirical data remains elusive. We faced this challenge by calculating entropy production in a multivariate Ornstein-Uhlenbeck process fitted to fMRI brain activity recordings. To test this approach, we focused on the transition from wakefulness to deep sleep, revealing a monotonous relationship b…
We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional modulation in earlier studies that used high-grade equipment in laboratory settings. Here we reproduce many of the results from these studies using portable low-cost equipment, focusing on the robustness of using ISC for subjects experiencing naturalistic stimuli. The present data shows that stimulus-ev…
Cardiff University（卡迪夫大学）；University of the West of England（西英格兰大学）
The classification of electroencephalography (EEG) signals is useful in a wide range of applications such as seizure detection/prediction, motor imagery classification, emotion classification and drug effects diagnosis, amongst others. With the large number of EEG channels acquired, it has become vital that efficient data-reduction methods are developed, with varying importance from one application to another. It is also important that online classification is achieved during EEG recording for many applications, to monitor changes as they happen. In this paper we introduce a method based on Mu…
The resistive or non-resistive nature of the extracellular space in the brain is still debated, and is an important issue for correctly modeling extracellular potentials. Here, we first show theoretically that if the medium is resistive, the frequency scaling should be the same for electroencephalogram (EEG) and magnetoencephalogram (MEG) signals at low frequencies (<10 Hz). To test this prediction, we analyzed the spectrum of simultaneous EEG and MEG measurements in four human subjects. The frequency scaling of EEG displays coherent variations across the brain, in general between 1/f and 1/f^…
In this viewpoint article, we discuss the electric properties of the medium around neurons, which are important to correctly interpret extracellular potentials or electric field effects in neural tissue. We focus on how these electric properties shape the frequency scaling of brain signals at different scales, such as intracellular recordings, the local field potential (LFP), the electroencephalogram (EEG) or the magnetoencephalogram (MEG). These signals display frequency-scaling properties which are not consistent with resistive media. The medium appears to exert a frequency filtering scaling…
We described driver behaviour and brain dynamics acquired from a 90-minute sustained-attention task in an immersive driving simulator. The data include 62 copies of 32 channel electroencephalography (EEG) data for 27 subjects that drove on a four lane highway and were asked to keep the car cruising in the centre of the lane. Lane departure events were randomly induced to make the car drift from the original cruising lane towards the left or right lane. A complete trial includes events with deviation onset, response onset, and response offset. The next trial, in which the subject has to drive b…
Objective: The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact caused by cardiac induced movement contaminates the EEG during EEG-fMRI recordings. Removal of BCG in software has generally made use of linear decompositions of the corrupted EEG. This is not ideal as the BCG signal is non-stationary and propagates in a manner which is non-linearly dependent on …
"Flow" is a hyper-engaged state of consciousness most commonly described in athletics, popularly termed "being in the zone." Quantitative research into flow has been hampered by the disruptive nature of gathering subjective reports. Here we show that a passive probe (suppression of Auditory Evoked Potential in EEG) that allowed our participants to remain engaged in a first-person shooting game while we continually tracked the depth of their immersion corresponded with the participants' subjective experiences, and with their objective performance levels. Comparing this time-varying record of fl…
The extent of intra-individual and inter-individual variability is an important factor in determining the statistical, and hence possibly clinical, significance of observed differences in the EEG. This study investigates the changes in classical quantitative EEG (qEEG) measures, as well as of parameters obtained by fitting frequency spectra to a continuum model of brain electrical activity. These parameters may have extra variability due to model selection and fitting. Besides estimating levels of intra-individual and inter-individual variability, we determine approximate time scales for chang…
Goal: Limited-channel EEG research in neonates is hindered by lack of open, accessible analytic tools. To overcome this limitation, we have created the Washington University- Neonatal EEG Analysis Toolbox (WU-NEAT), containing two of the most commonly used tools, provided in an open-source, clinically-validated package running within MATLAB. Methods: The first algorithm is the amplitude-integrated EEG (aEEG), which is generated by filtering, rectifying and time-compressing the original EEG recording, with subsequent semi-logarithmic display. The second algorithm is the spectral edge frequency …
$\textbf{Objective}$: To assess the validity of an automatic EEG arousal detection algorithm using large patient samples and different heterogeneous databases $\textbf{Methods}$: Automatic scorings were confronted with results from human expert scorers on a total of 2768 full-night PSG recordings obtained from two different databases. Of them, 472 recordings were obtained during clinical routine at our sleep center and were subdivided into two subgroups of 220 (HMC-S) and 252 (HMC-M) recordings each, attending to the procedure followed by the clinical expert during the visual review (semi-auto…
There are several protocols in the Electroencephalography (EEG) recording scenarios which produce various types of event-related potentials (ERP). P300 pattern is a well-known ERP which produced by auditory and visual oddball paradigm and BCI speller system. In this study, P300 and non-P300 separability are investigated in two scenarios including image recognition paradigm and BCI speller. Image recognition scenario is an experiment that examines the participants, knowledge about an image that shown to them before by analyzing the EEG signal recorded during the observing of that image as visua…
Generally, blinks are treated on equal with artifacts and noise while analyzing EEG signals. However, blinks carry important information about mental processes and thus it is important to detect blinks accurately. The aim of the presented study is to propose a blink detection method and discuss its application for extracting blink rate variability, a novel concept that might shed some light on the mental processes. In this study, 14 EEG recordings were selected for assessing the quality of the proposed blink detection algorithm.
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Ferrara（费拉拉大学）
Transcranial Magnetic Stimulation (TMS) combined with EEG recordings (TMS-EEG) has shown great potential in the study of the brain and in particular of Alzheimer's Disease (AD). In this study, we propose an automatic method of determining the duration of TMS induced perturbation of the EEG signal as a potential metric reflecting the brain's functional alterations. A preliminary study is conducted in patients with Alzheimer's disease (AD). Three metrics for characterizing the strength and duration of TMS evoked EEG (TEP) activity are proposed and their potential in identifying AD patients from …
University of Basel（巴塞尔大学）；Hospital of the University of Basel（巴塞尔大学医院）
Recordings of electrical brain activity carry information about a person's cognitive health. For recording EEG signals, a very common setting is for a subject to be at rest with its eyes closed. Analysis of these recordings often involve a dimensionality reduction step in which electrodes are grouped into 10 or more regions (depending on the number of electrodes available). Then an average over each group is taken which serves as a feature in subsequent evaluation. Currently, the most prominent features used in clinical practice are based on spectral power densities. In our work we consider a …
This paper proposes a practical approach to addressing limitations posed by use of single active electrodes in applications for sleep stage classification. Electroencephalography (EEG)-based characterizations of sleep stage progression contribute the diagnosis and monitoring of the many pathologies of sleep. Several prior reports have explored ways of automating the analysis of sleep EEG and of reducing the complexity of the data needed for reliable discrimination of sleep stages in order to make it possible to perform sleep studies at lower cost in the home (rather than only in specialized cl…
Automatic detection of abnormal clinical EEG: comparison of a finetuned foundation model with two deep learning models
BioSerenity；Université de Lille（里尔大学）；CHU de Lille（里尔大学医院）；Hôpital Universitaire de Bruxelles（布鲁塞尔大学医院）；Université Libre de Bruxelles（布鲁塞尔自由大学）；Yale University School of Medicine（耶鲁大学医学院）；Université de Lorraine（洛林大学）；CHRU-Nancy（南锡大学地区医院）；CHU Grenoble Alpes（格勒诺布尔大学医院）；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Jolt Capital
Institute of Neuroinformatics, University of Zurich and ETH Zurich（苏黎世大学与苏黎世联邦理工学院神经信息学研究所）；UniversitätsSpital und Universität Zürich（苏黎世大学医院与苏黎世大学）
In this work, we present a neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neural network (SNN) architecture on the same die for recording, processing, and detecting High Frequency Oscillations (HFO), which are biomarkers for the epileptogenic zone. The device was fabricated using a standard 0.18$μ$m CMOS technology node and has a total area of 99mm$^{2}$. We demonstrate its application to HFO detection in the iEEG recorded from 9 patients with temporal lobe epilepsy who subsequently underwent …
Spatially Masked Regression Reveals Local and Distributed Predictability in Electrophysiological Recordings
Department of Electrical and Computer Engineering, IUT（电气与计算机工程系）；McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT)（脑科学研究所，麻省理工学院（MIT））
