Shifted Coupling of EEG Driving Frequencies and fMRI Resting State Networks in Schizophrenia Spectrum Disorders

INTRODUCTION: The cerebral resting state in schizophrenia is altered, as has been demonstrated separately by electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) resting state networks (RSNs). Previous simultaneous EEG/fMRI findings in healthy controls suggest that a consistent spatiotemporal coupling between neural oscillations (EEG frequency correlates) and RSN activity is necessary to organize cognitive processes optimally. We hypothesized that this coupling is disorganized in schizophrenia and related psychotic disorders, in particular regarding higher cognitive RSNs such as the default-mode (DMN) and left-working-memory network (LWMN). METHODS: Resting state was investigated in eleven patients with a schizophrenia spectrum disorder (n = 11) and matched healthy controls (n = 11) using simultaneous EEG/fMRI. The temporal association of each RSN to topographic spectral changes in the EEG was assessed by creating Covariance Maps. Group differences within, and group similarities across frequencies were estimated for the Covariance Maps. RESULTS: The coupling of EEG frequency bands to the DMN and the LWMN respectively, displayed significant similarities that were shifted towards lower EEG frequencies in patients compared to healthy controls. CONCLUSIONS: By combining EEG and fMRI, each measuring different properties of the same pathophysiology, an aberrant relationship between EEG frequencies and altered RSNs was observed in patients. RSNs of patients were related to lower EEG frequencies, indicating functional alterations of the spatiotemporal coupling. SIGNIFICANCE: The finding of a deviant and shifted coupling between RSNs and related EEG frequencies in patients with a schizophrenia spectrum disorder is significant, as it might indicate how failures in the processing of internal and external stimuli, as commonly seen during this symptomatology (i.e. thought disorders, hallucinations), arise.

Neural correlate of spatial presence in an arousing and noninteractive virtual reality: an EEG and psychophysiology study

Using electroencephalography (EEG), psychophysiology, and psychometric measures, this is the first study which investigated the neurophysiological underpinnings of spatial presence. Spatial presence is considered a sense of being physically situated within a spatial environment portrayed by a medium (e.g., television, virtual reality). Twelve healthy children and 11 healthy adolescents were watching different virtual roller coaster scenarios. During a control session, the roller coaster cab drove through a horizontal roundabout track. The following realistic roller coaster rides consisted of spectacular ups, downs, and loops. Low-resolution brain electromagnetic tomography (LORETA) and event-related desynchronization (ERD) were used to analyze the EEG data. As expected, we found that, compared to the control condition, experiencing a virtual roller coaster ride evoked in both groups strong SP experiences, increased electrodermal reactions, and activations in parietal brain areas known to be involved in spatial navigation. In addition, brain areas that receive homeostatic afferents from somatic and visceral sensations of the body were strongly activated. Most interesting, children (as compared to adolescents) reported higher spatial presence experiences and demonstrated a different frontal activation pattern. While adolescents showed increased activation in prefrontal areas known to be involved in the control of executive functions, children demonstrated a decreased activity in these brain regions. Interestingly, recent neuroanatomical and neurophysiological studies have shown that the frontal brain continues to develop to adult status well into adolescence. Thus, the result of our study implies that the increased spatial presence experience in children may result from the not fully developed control functions of the frontal cortex.

Zazen meditation and no-task resting EEG compared with LORETA intracortical source localization

Meditation is a self-induced and willfully initiated practice that alters the state of consciousness. The meditation practice of Zazen, like many other meditation practices, aims at disregarding intrusive thoughts while controlling body posture. It is an open monitoring meditation characterized by detached moment-to-moment awareness and reduced conceptual thinking and self-reference. Which brain areas differ in electric activity during Zazen compared to task-free resting? Since scalp electroencephalography (EEG) waveforms are reference-dependent, conclusions about the localization of active brain areas are ambiguous. Computing intracerebral source models from the scalp EEG data solves this problem. In the present study, we applied source modeling using low resolution brain electromagnetic tomography (LORETA) to 58-channel scalp EEG data recorded from 15 experienced Zen meditators during Zazen and no-task resting. Zazen compared to no-task resting showed increased alpha-1 and alpha-2 frequency activity in an exclusively right-lateralized cluster extending from prefrontal areas including the insula to parts of the somatosensory and motor cortices and temporal areas. Zazen also showed decreased alpha and beta-2 activity in the left angular gyrus and decreased beta-1 and beta-2 activity in a large bilateral posterior cluster comprising the visual cortex, the posterior cingulate cortex and the parietal cortex. The results include parts of the default mode network and suggest enhanced automatic memory and emotion processing, reduced conceptual thinking and self-reference on a less judgmental, i.e., more detached moment-to-moment basis during Zazen compared to no-task resting.

Altered directed functional connectivity in temporal lobe epilepsy in the absence of interictal spikes: A high density EEG study.

OBJECTIVE In patients with epilepsy, seizure relapse and behavioral impairments can be observed despite the absence of interictal epileptiform discharges (IEDs). Therefore, the characterization of pathologic networks when IEDs are not present could have an important clinical value. Using Granger-causal modeling, we investigated whether directed functional connectivity was altered in electroencephalography (EEG) epochs free of IED in left and right temporal lobe epilepsy (LTLE and RTLE) compared to healthy controls. METHODS Twenty LTLE, 20 RTLE, and 20 healthy controls underwent a resting-state high-density EEG recording. Source activity was obtained for 82 regions of interest (ROIs) using an individual head model and a distributed linear inverse solution. Granger-causal modeling was applied to the source signals of all ROIs. The directed functional connectivity results were compared between groups and correlated with clinical parameters (duration of the disease, age of onset, age, and learning and mood impairments). RESULTS We found that: (1) patients had significantly reduced connectivity from regions concordant with the default-mode network; (2) there was a different network pattern in patients versus controls: the strongest connections arose from the ipsilateral hippocampus in patients and from the posterior cingulate cortex in controls; (3) longer disease duration was associated with lower driving from contralateral and ipsilateral mediolimbic regions in RTLE; (4) aging was associated with a lower driving from regions in or close to the piriform cortex only in patients; and (5) outflow from the anterior cingulate cortex was lower in patients with learning deficits or depression compared to patients without impairments and to controls. SIGNIFICANCE Resting-state network reorganization in the absence of IEDs strengthens the view of chronic and progressive network changes in TLE. These resting-state connectivity alterations could constitute an important biomarker of TLE, and hold promise for using EEG recordings without IEDs for diagnosis or prognosis of this disorder.

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The focus of this chapter is to study feature extraction and pattern classification methods from two medical areas, Stabilometry and Electroencephalography (EEG). Stabilometry is the branch of medicine responsible for examining balance in human beings. Balance and dizziness disorders are probably two of the most common illnesses that physicians have to deal with. In Stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods are known as events. In this chapter, two feature extraction schemes have been developed to identify and characterise the events in Stabilometry and EEG signals. Based on these extracted features, an Adaptive Fuzzy Inference Neural network has been applied for classification of Stabilometry and EEG signals.

TMS- EEG Signatures of GABAergic Neurotransmission in the Human Cortex

Combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG) constitutes a powerful tool to directly assess human cortical excitability and connectivity. TMS of the primary motor cortex elicits a sequence of TMS-evoked EEG potentials (TEPs). It is thought that inhibitory neurotransmission through GABA-A receptors (GABAAR) modulates early TEPs (<50 ms after TMS), whereas GABA-B receptors (GABABR) play a role for later TEPs (at ∼100 ms after TMS). However, the physiological underpinnings of TEPs have not been clearly elucidated yet. Here, we studied the role of GABAA/B-ergic neurotransmission for TEPs in healthy subjects using a pharmaco-TMS-EEG approach. In Experiment 1, we tested the effects of a single oral dose of alprazolam (a classical benzodiazepine acting as allosteric-positive modulator at α1, α2, α3, and α5 subunit-containing GABAARs) and zolpidem (a positive modulator mainly at the α1 GABAAR) in a double-blind, placebo-controlled, crossover study. In Experiment 2, we tested the influence of baclofen (a GABABR agonist) and diazepam (a classical benzodiazepine) versus placebo on TEPs. Alprazolam and diazepam increased the amplitude of the negative potential at 45 ms after stimulation (N45) and decreased the negative component at 100 ms (N100), whereas zolpidem increased the N45 only. In contrast, baclofen specifically increased the N100 amplitude. These results provide strong evidence that the N45 represents activity of α1-subunit-containing GABAARs, whereas the N100 represents activity of GABABRs. Findings open a novel window of opportunity to study alteration of GABAA-/GABAB-related inhibition in disorders, such as epilepsy or schizophrenia.

Comparison of the distributions of classical and adaptively aligned EEG power spectra

The distributions of eyes-closed resting electroencephalography (EEG) power spectra and their residuals were described and compared using classically averaged and adaptively aligned averaged spectra. Four minutes of eyes-closed resting EEG was available from 69 participants. Spectra were calculated with 0.5-Hz resolution and were analyzed at this level. It was shown that power in the individual 0.5 Hz frequency bins can be considered normally distributed when as few as three or four 2-second epochs of EEG are used in the average. A similar result holds for the residuals. Power at the peak Alpha frequency has quite different statistical behaviour to power at other frequencies and it is considered that power at peak Alpha represents a relatively individuated process that is best measured through aligned averaging. Previous analyses of contrasts in upper and lower alpha bands may be explained in terms of the variability or distribution of the peak Alpha frequency itself.

Methodology of photic stimulation revisited:updated European algorithm for visual stimulation in the EEG laboratory

Intermittent photic stimulation (IPS) is a common procedure performed in the electroencephalography (EEG) laboratory in children and adults to detect abnormal epileptogenic sensitivity to flickering light (i.e., photosensitivity). In practice, substantial variability in outcome is anecdotally found due to the many different methods used per laboratory and country. We believe that standardization of procedure, based on scientific and clinical data, should permit reproducible identification and quantification of photosensitivity. We hope that the use of our new algorithm will help in standardizing the IPS procedure, which in turn may more clearly identify and assist monitoring of patients with epilepsy and photosensitivity. Our algorithm goes far beyond that published in 1999 (Epilepsia, 1999a, 40, 75; Neurophysiol Clin, 1999b, 29, 318): it has substantially increased content, detailing technical and logistical aspects of IPS testing and the rationale for many of the steps in the IPS procedure. Furthermore, our latest algorithm incorporates the consensus of repeated scientific meetings of European experts in this field over a period of 6 years with feedback from general neurologists and epileptologists to improve its validity and utility. Accordingly, our European group has provided herein updated algorithms for two different levels of methodology: (1) requirements for defining photosensitivity in patients and in family members of known photosensitive patients and (2) requirements for tailored studies in patients with a clear history of visually induced seizures or complaints, and in those already known to be photosensitive.

Classificador máquina de suporte vetorial com análise de Fourier aplicada em dados de EEG e EMG

The classifier support vector machine is used in several problems in various areas of knowledge. Basically the method used in this classier is to end the hyperplane that maximizes the distance between the groups, to increase the generalization of the classifier. In this work, we treated some problems of binary classification of data obtained by electroencephalography (EEG) and electromyography (EMG) using Support Vector Machine with some complementary techniques, such as: Principal Component Analysis to identify the active regions of the brain, the periodogram method which is obtained by Fourier analysis to help discriminate between groups and Simple Moving Average to eliminate some of the existing noise in the data. It was developed two functions in the software R, for the realization of training tasks and classification. Also, it was proposed two weights systems and a summarized measure to help on deciding in classification of groups. The application of these techniques, weights and the summarized measure in the classier, showed quite satisfactory results, where the best results were an average rate of 95.31% to visual stimuli data, 100% of correct classification for epilepsy data and rates of 91.22% and 96.89% to object motion data for two subjects.

Entropy feature extraction of EEG signals for automatic person identification

The use of human brain electroencephalography (EEG) signals for automatic person identi cation has been investigated for a decade. It has been found that the performance of an EEG-based person identication system highly depends on what feature to be extracted from multi-channel EEG signals. Linear methods such as Power Spectral Density and Autoregressive Model have been used to extract EEG features. However these methods assumed that EEG signals are stationary. In fact, EEG signals are complex, non-linear, non-stationary, and random in nature. In addition, other factors such as brain condition or human characteristics may have impacts on the performance, however these factors have not been investigated and evaluated in previous studies. It has been found in the literature that entropy is used to measure the randomness of non-linear time series data. Entropy is also used to measure the level of chaos of braincomputer interface systems. Therefore, this thesis proposes to study the role of entropy in non-linear analysis of EEG signals to discover new features for EEG-based person identi- cation. Five dierent entropy methods including Shannon Entropy, Approximate Entropy, Sample Entropy, Spectral Entropy, and Conditional Entropy have been proposed to extract entropy features that are used to evaluate the performance of EEG-based person identication systems and the impacts of epilepsy, alcohol, age and gender characteristics on these systems. Experiments were performed on the Australian EEG and Alcoholism datasets. Experimental results have shown that, in most cases, the proposed entropy features yield very fast person identication, yet with compatible accuracy because the feature dimension is low. In real life security operation, timely response is critical. The experimental results have also shown that epilepsy, alcohol, age and gender characteristics have impacts on the EEG-based person identication systems.

Using electroencephalography signals to control acoustical processing

Current hearing-assistive technology performs poorly in noisy multi-talker conditions. The goal of this thesis was to establish the feasibility of using EEG to guide acoustic processing in such conditions. To attain this goal, this research developed a model via the constructive research method, relying on literature review. Several approaches have revealed improvements in the performance of hearing-assistive devices under multi-talker conditions, namely beamforming spatial filtering, model-based sparse coding shrinkage, and onset enhancement of the speech signal. Prior research has shown that electroencephalography (EEG) signals contain information that concerns whether the person is actively listening, what the listener is listening to, and where the attended sound source is. This thesis constructed a model for using EEG information to control beamforming, model-based sparse coding shrinkage, and onset enhancement of the speech signal. The purpose of this model is to propose a framework for using EEG signals to control sound processing to select a single talker in a noisy environment containing multiple talkers speaking simultaneously. On a theoretical level, the model showed that EEG can control acoustical processing. An analysis of the model identified a requirement for real-time processing and that the model inherits the computationally intensive properties of acoustical processing, although the model itself is low complexity placing a relatively small load on computational resources. A research priority is to develop a prototype that controls hearing-assistive devices with EEG. This thesis concludes highlighting challenges for future research.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support Vector Machines (SVMs) are widely used classifiers for detecting physiological patterns in Human-Computer Interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the application of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables, and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

Predicting driver's hypovigilance on monotonous roads: literature review.

Drivers' ability to react to unpredictable events deteriorates when exposed to highly predictable and uneventful driving tasks. Particularly, highway design reduces the driving task mainly to a lane-keeping one. It contributes to hypovigilance and road crashes as drivers are often not aware that their driving behaviour is impaired. Monotony increases fatigue, however, the fatigue community has mainly focused on endogenous factors leading to fatigue such as sleep deprivation. This paper focuses on the exogenous factor monotony which contributes to hypovigilance. Objective measurements of the effects of monotonous driving conditions on the driver and the vehicle's dynamics is systematically reviewed with the aim of justifying the relevance of the need for a mathematical framework that could predict hypovigilance in real-time. Although electroencephalography (EEG) is one of the most reliable measures of vigilance, it is obtrusive. This suggests to predict from observable variables the time when the driver is hypovigilant. Outlined is a vision for future research in the modelling of driver vigilance decrement due to monotonous driving conditions. A mathematical model for predicting drivers’ hypovigilance using information like lane positioning, steering wheel movements and eye blinks is provided. Such a modelling of driver vigilance should enable the future development of an in-vehicle device that detects driver hypovigilance in advance, thus offering the potential to enhance road safety and prevent road crashes.

Predicting effects of monotony on driver’s vigilance

Monotony has been identified as a contributing factor to road crashes. Drivers’ ability to react to unpredictable events deteriorates when exposed to highly predictable and uneventful driving tasks, such as driving on Australian rural roads, many of which are monotonous by nature. Highway design in particular attempts to reduce the driver’s task to a merely lane-keeping one. Such a task provides little stimulation and is monotonous, thus affecting the driver’s attention which is no longer directed towards the road. Inattention contributes to crashes, especially for professional drivers. Monotony has been studied mainly from the endogenous perspective (for instance through sleep deprivation) without taking into account the influence of the task itself (repetitiveness) or the surrounding environment. The aim and novelty of this thesis is to develop a methodology (mathematical framework) able to predict driver lapses of vigilance under monotonous environments in real time, using endogenous and exogenous data collected from the driver, the vehicle and the environment. Existing approaches have tended to neglect the specificity of task monotony, leaving the question of the existence of a “monotonous state” unanswered. Furthermore the issue of detecting vigilance decrement before it occurs (predictions) has not been investigated in the literature, let alone in real time. A multidisciplinary approach is necessary to explain how vigilance evolves in monotonous conditions. Such an approach needs to draw on psychology, physiology, road safety, computer science and mathematics. The systemic approach proposed in this study is unique with its predictive dimension and allows us to define, in real time, the impacts of monotony on the driver’s ability to drive. Such methodology is based on mathematical models integrating data available in vehicles to the vigilance state of the driver during a monotonous driving task in various environments. The model integrates different data measuring driver’s endogenous and exogenous factors (related to the driver, the vehicle and the surrounding environment). Electroencephalography (EEG) is used to measure driver vigilance since it has been shown to be the most reliable and real time methodology to assess vigilance level. There are a variety of mathematical models suitable to provide a framework for predictions however, to find the most accurate model, a collection of mathematical models were trained in this thesis and the most reliable was found. The methodology developed in this research is first applied to a theoretically sound measure of sustained attention called Sustained Attention Response to Task (SART) as adapted by Michael (2010), Michael and Meuter (2006, 2007). This experiment induced impairments due to monotony during a vigilance task. Analyses performed in this thesis confirm and extend findings from Michael (2010) that monotony leads to an important vigilance impairment independent of fatigue. This thesis is also the first to show that monotony changes the dynamics of vigilance evolution and tends to create a “monotonous state” characterised by reduced vigilance. Personality traits such as being a low sensation seeker can mitigate this vigilance decrement. It is also evident that lapses in vigilance can be predicted accurately with Bayesian modelling and Neural Networks. This framework was then applied to the driving task by designing a simulated monotonous driving task. The design of such task requires multidisciplinary knowledge and involved psychologist Rebecca Michael. Monotony was varied through both the road design and the road environment variables. This experiment demonstrated that road monotony can lead to driving impairment. Particularly monotonous road scenery was shown to have the most impact compared to monotonous road design. Next, this study identified a variety of surrogate measures that are correlated with vigilance levels obtained from the EEG. Such vigilance states can be predicted with these surrogate measures. This means that vigilance decrement can be detected in a car without the use of an EEG device. Amongst the different mathematical models tested in this thesis, only Neural Networks predicted the vigilance levels accurately. The results of both these experiments provide valuable information about the methodology to predict vigilance decrement. Such an issue is quite complex and requires modelling that can adapt to highly inter-individual differences. Only Neural Networks proved accurate in both studies, suggesting that these models are the most likely to be accurate when used on real roads or for further research on vigilance modelling. This research provides a better understanding of the driving task under monotonous conditions. Results demonstrate that mathematical modelling can be used to determine the driver’s vigilance state when driving using surrogate measures identified during this study. This research has opened up avenues for future research and could result in the development of an in-vehicle device predicting driver vigilance decrement. Such a device could contribute to a reduction in crashes and therefore improve road safety.

Use of brain computer interface to drive : preliminary results

This paper reports on the implementation of a non-invasive electroencephalography-based brain-computer interface to control functions of a car in a driving simulator. The system is comprised of a Cleveland Medical Devices BioRadio 150 physiological signal recorder, a MATLAB-based BCI and an OKTAL SCANeR advanced driving experience simulator. The system utilizes steady-state visual-evoked potentials for the BCI paradigm, elicited by frequency-modulated high-power LEDs and recorded with the electrode placement of Oz-Fz with Fz as ground. A three-class online brain-computer interface was developed and interfaced with an advanced driving simulator to control functions of the car, including acceleration and steering. The findings are mainly exploratory but provide an indication of the feasibility and challenges of brain-controlled on-road cars for the future, in addition to a safe, simulated BCI driving environment to use as a foundation for research into overcoming these challenges.