Adaptive tracking of EEG oscillations.

Neuronal oscillations are an important aspect of EEG recordings. These oscillations are supposed to be involved in several cognitive mechanisms. For instance, oscillatory activity is considered a key component for the top-down control of perception. However, measuring this activity and its influence requires precise extraction of frequency components. This processing is not straightforward. Particularly, difficulties with extracting oscillations arise due to their time-varying characteristics. Moreover, when phase information is needed, it is of the utmost importance to extract narrow-band signals. This paper presents a novel method using adaptive filters for tracking and extracting these time-varying oscillations. This scheme is designed to maximize the oscillatory behavior at the output of the adaptive filter. It is then capable of tracking an oscillation and describing its temporal evolution even during low amplitude time segments. Moreover, this method can be extended in order to track several oscillations simultaneously and to use multiple signals. These two extensions are particularly relevant in the framework of EEG data processing, where oscillations are active at the same time in different frequency bands and signals are recorded with multiple sensors. The presented tracking scheme is first tested with synthetic signals in order to highlight its capabilities. Then it is applied to data recorded during a visual shape discrimination experiment for assessing its usefulness during EEG processing and in detecting functionally relevant changes. This method is an interesting additional processing step for providing alternative information compared to classical time-frequency analyses and for improving the detection and analysis of cross-frequency couplings.

The Brain as a Distributed Intelligent Processing System: An EEG Study

Background: Various neuroimaging studies, both structural and functional, have provided support for the proposal that a distributed brain network is likely to be the neural basis of intelligence. The theory of Distributed Intelligent Processing Systems (DIPS), first developed in the field of Artificial Intelligence, was proposed to adequately model distributed neural intelligent processing. In addition, the neural efficiency hypothesis suggests that individuals with higher intelligence display more focused cortical activation during cognitive performance, resulting in lower total brain activation when compared with individuals who have lower intelligence. This may be understood as a property of the DIPS. Methodology and Principal Findings: In our study, a new EEG brain mapping technique, based on the neural efficiency hypothesis and the notion of the brain as a Distributed Intelligence Processing System, was used to investigate the correlations between IQ evaluated with WAIS (Whechsler Adult Intelligence Scale) and WISC (Wechsler Intelligence Scale for Children), and the brain activity associated with visual and verbal processing, in order to test the validity of a distributed neural basis for intelligence. Conclusion: The present results support these claims and the neural efficiency hypothesis.

Optimization of fMRI Processing Parameters for Simutaneous Acquisition of EEG/fMRI in Focal Epilepsy

In the context of focal epilepsy, the simultaneous combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) holds a great promise as a technique by which the hemodynamic correlates of interictal spikes detected on scalp EEG can be identified. The fact that traditional EEG recordings have not been able to overcome the difficulty in correlating the ictal clinical symptoms to the onset in particular areas of the lobes, brings the need of mapping with more precision the epileptogenic cortical regions. On the other hand, fMRI suggested localizations more consistent with the ictal clinical manifestations detected. This study was developed in order to improve the knowledge about the way parameters involved in the physical and mathematical data, produced by the EEG/fMRI technique processing, would influence the final results. The evaluation of the accuracy was made by comparing the BOLD results with: the high resolution EEG maps; the malformative lesions detected in the T1 weighted MR images; and the anatomical localizations of the diagnosed symptomatology of each studied patient. The optimization of the set of parameters used, will provide an important contribution to the diagnosis of epileptogenic focuses, in patients included on an epilepsy surgery evaluation program. The results obtained allowed us to conclude that: by associating the BOLD effect with interictal spikes, the epileptogenic areas are mapped to localizations different from those obtained by the EEG maps representing the electrical potential distribution across the scalp (EEG); there is an important and solid bond between the variation of particular parameters (manipulated during the fMRI data processing) and the optimization of the final results, from which smoothing, deleted volumes, HRF (used to convolve with the activation design), and the shape of the Gamma function can be certainly emphasized.

A Nonstationary Model of Newborn EEG

The detection of seizure in the newborn is a critical aspect of neurological research. Current automatic detection techniques are difficult to assess due to the problems associated with acquiring and labelling newborn electroencephalogram (EEG) data. A realistic model for newborn EEG would allow confident development, assessment and comparison of these detection techniques. This paper presents a model for newborn EEG that accounts for its self-similar and non-stationary nature. The model consists of background and seizure sub-models. The newborn EEG background model is based on the short-time power spectrum with a time-varying power law. The relationship between the fractal dimension and the power law of a power spectrum is utilized for accurate estimation of the short-time power law exponent. The newborn EEG seizure model is based on a well-known time-frequency signal model. This model addresses all significant time-frequency characteristics of newborn EEG seizure which include; multiple components or harmonics, piecewise linear instantaneous frequency laws and harmonic amplitude modulation. Estimates of the parameters of both models are shown to be random and are modelled using the data from a total of 500 background epochs and 204 seizure epochs. The newborn EEG background and seizure models are validated against real newborn EEG data using the correlation coefficient. The results show that the output of the proposed models has a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively).

EEG-based Brain-Computer Interfaces: An Overview of Basic Concepts and Clinical Applications in Neurorehabilitation

Some patients are no longer able to communicate effectively or even interact with the outside world in ways that most of us take for granted. In the most severe cases, tetraplegic or post-stroke patients are literally `locked in` their bodies, unable to exert any motor control after, for example, a spinal cord injury or a brainstem stroke, requiring alternative methods of communication and control. But we suggest that, in the near future, their brains may offer them a way out. Non-invasive electroencephalogram (EEG)-based brain-computer interfaces (BCD can be characterized by the technique used to measure brain activity and by the way that different brain signals are translated into commands that control an effector (e.g., controlling a computer cursor for word processing and accessing the internet). This review focuses on the basic concepts of EEG-based BC!, the main advances in communication, motor control restoration and the down-regulation of cortical activity, and the mirror neuron system (MNS) in the context of BCI. The latter appears to be relevant for clinical applications in the coming years, particularly for severely limited patients. Hypothetically, MNS could provide a robust way to map neural activity to behavior, representing the high-level information about goals and intentions of these patients. Non-invasive EEG-based BCIs allow brain-derived communication in patients with amyotrophic lateral sclerosis and motor control restoration in patients after spinal cord injury and stroke. Epilepsy and attention deficit and hyperactive disorder patients were able to down-regulate their cortical activity. Given the rapid progression of EEG-based BCI research over the last few years and the swift ascent of computer processing speeds and signal analysis techniques, we suggest that emerging ideas (e.g., MNS in the context of BC!) related to clinical neuro-rehabilitation of severely limited patients will generate viable clinical applications in the near future.

Preprocessing and time-frequency analysis of newborn EEG seizures

Neurological disease or dysfunction in newborn infants is often first manifested by seizures. Prolonged seizures can result in impaired neurodevelopment or even death. In adults, the clinical signs of seizures are well defined and easily recognized. In newborns, however, the clinical signs are subtle and may be absent or easily missed without constant close observation. This article describes the use of adaptive signal processing techniques for removing artifacts from newborn electroencephalogram (EEG) signals. Three adaptive algorithms have been designed in the context of EEG signals. This preprocessing is necessary before attempting a fine time-frequency analysis of EEG rhythmical activities, such as electrical seizures, corrupted by high amplitude signals. After an overview of newborn EEG signals, the authors describe the data acquisition set-up. They then introduce the basic physiological concepts related to normal and abnormal newborn EEGs and discuss the three adaptive algorithms for artifact removal. They also present time-frequency representations (TFRs) of seizure signals and discuss the estimation and modeling of the instantaneous frequency related to the main ridge of the TFR.

A Wireless EEG Acquisition Platform based on Embedded Systems

This paper proposes a wireless EEG acquisition platform based on Open Multimedia Architecture Platform (OMAP) embedded system. A high-impedance active dry electrode was tested for improving the scalp- electrode interface. It was used the sigma-delta ADS1298 analog-to-digital converter, and developed a “kernelspace” character driver to manage the communications between the converter unit and the OMAP’s ARM core. The acquired EEG signal data is processed by a “userspace” application, which accesses the driver’s memory, saves the data to a SD-card and transmits them through a wireless TCP/IP-socket to a PC. The electrodes were tested through the alpha wave replacement phenomenon. The experimental results presented the expected alpha rhythm (8-13 Hz) reactiveness to the eyes opening task. The driver spends about 725 μs to acquire and store the data samples. The application takes about 244 μs to get the data from the driver and 1.4 ms to save it in the SD-card. A WiFi throughput of 12.8Mbps was measured which results in a transmission time of 5 ms for 512 kb of data. The embedded system consumes about 200 mAh when wireless off and 400 mAh when it is on. The system exhibits a reliable performance to record EEG signals and transmit them wirelessly. Besides the microcontroller-based architectures, the proposed platform demonstrates that powerful ARM processors running embedded operating systems can be programmed with real-time constrains at the kernel level in order to control hardware, while maintaining their parallel processing abilities in high level software applications.

Comparison of Methods and Co-Registration Maps of EEG and fMRI in Occipital Lobe Epilepsy

Clinically childhood occipital lobe epilepsy (OLE) manifests itself with distinct syndromes. The traditional EEG recordings have not been able to overcome the difficulty in correlating the ictal clinical symptoms to the onset in particular areas of the occipital lobes. To understand these syndromes it is important to map with more precision the epileptogenic cortical regions in OLE. Experimentally, we studied three idiopathic childhood OLE patients with EEG source analysis and with the simultaneous acquisition of EEG and fMRI, to map the BOLD effect associated with EEG spikes. The spatial overlap between the EEG and BOLD results was not very good, but the fMRI suggested localizations more consistent with the ictal clinical manifestations of each type of epileptic syndrome. Since our first results show that by associating the BOLD effect with interictal spikes the epileptogenic areas are mapped to localizations different from those calculated from EEG sources and that by using different EEG/fMRI processing methods our results differ to some extent, it is very important to compare the different methods of processing the localization of activation and develop a good methodology for obtaining co-registration maps of high resolution EEG with BOLD localizations.

EEG-based functional brain networks: does the network size matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.

Robust discrimination between EEG responses to categories of environmental sounds in early coma.

Humans can recognize categories of environmental sounds, including vocalizations produced by humans and animals and the sounds of man-made objects. Most neuroimaging investigations of environmental sound discrimination have studied subjects while consciously perceiving and often explicitly recognizing the stimuli. Consequently, it remains unclear to what extent auditory object processing occurs independently of task demands and consciousness. Studies in animal models have shown that environmental sound discrimination at a neural level persists even in anesthetized preparations, whereas data from anesthetized humans has thus far provided null results. Here, we studied comatose patients as a model of environmental sound discrimination capacities during unconsciousness. We included 19 comatose patients treated with therapeutic hypothermia (TH) during the first 2 days of coma, while recording nineteen-channel electroencephalography (EEG). At the level of each individual patient, we applied a decoding algorithm to quantify the differential EEG responses to human vs. animal vocalizations as well as to sounds of living vocalizations vs. man-made objects. Discrimination between vocalization types was accurate in 11 patients and discrimination between sounds from living and man-made sources in 10 patients. At the group level, the results were significant only for the comparison between vocalization types. These results lay the groundwork for disentangling truly preferential activations in response to auditory categories, and the contribution of awareness to auditory category discrimination.

Adaptive filtering methods for identifying cross-frequency couplings in human EEG.

Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.

Age differences in the EEG N2 and P3 components during an interference task

The age-related increase in interference susceptibility has been well documented and largely attributed to a deficit in inhibition. In the present study, event-related potentials were used to investigate EEG correlates of inhibitory processing in an interference "Arrow" task. A specific interest was addressed to theN2 and P3 components that respectively refers to conflict monitoring and to efficiency of inhibition processes (Anguera et al,. 2011). Younger (N=10, Mage=24.6) and older (N=10, Mage=65.5) participants were invited to perform a task consisting in deciding, as fast and accurately as possible, whether an arrow presented on a computer screen points to the left or the right, irrespective of its position on the screen (left, middle or right). Responses were provided by key-presses using the left and right indexes. Three conditions were considered: congruent (arrow pointing to the same direction as that of the side of the screen on which it appears), incongruent (arrow pointing to the opposite direction), and neutral (arrow presented at the center of the screen). A total of 56 trials per conditions were performed. Behaviorally, the results showed that in the incongruent condition the percent of correct responses significantly decreased in both groups. After adjustment with simple RT (additional control task), the increased RTs obtained in the old group were significantly more pronounced in the incongruent condition. With respect to electrophysiological data, results showed that frontal site (Fz), the N2 amplitude was significantly larger for the younger as compared to the older (- 2.55 μV vs. -0.62 μV respectively) whatever the condition. At central site (Cz), the P3 amplitude significantly decreased in the older compared to the younger in the incongruent condition only. Our findings suggest that the increased RTs observed in older participants during the incongruent condition is more specifically linked to late cognitive resources involved in inhibiting prepotent response tendencies rather than associated with earlier stages of treatment dedicated to conflict monitoring.

Synchronization of EEG: bivariate and multivariate measures.

Synchronization behavior of electroencephalographic (EEG) signals is important for decoding information processing in the human brain. Modern multichannel EEG allows a transition from traditional measurements of synchronization in pairs of EEG signals to whole-brain synchronization maps. The latter can be based on bivariate measures (BM) via averaging over pair-wise values or, alternatively, on multivariate measures (MM), which directly ascribe a single value to the synchronization in a group. In order to compare BM versus MM, we applied nine different estimators to simulated multivariate time series with known parameters and to real EEGs.We found widespread correlations between BM and MM, which were almost frequency-independent for all the measures except coherence. The analysis of the behavior of synchronization measures in simulated settings with variable coupling strength, connection probability, and parameter mismatch showed that some of them, including S-estimator, S-Renyi, omega, and coherence, aremore sensitive to linear interdependences,while others, like mutual information and phase locking value, are more responsive to nonlinear effects. Onemust consider these properties together with the fact thatMM are computationally less expensive and, therefore, more efficient for the large-scale data sets than BM while choosing a synchronization measure for EEG analysis.

Decoding stimulus-related information from single-trial EEG responses based on voltage topographies

Neuroimaging studies typically compare experimental conditions using average brain responses, thereby overlooking the stimulus-related information conveyed by distributed spatio-temporal patterns of single-trial responses. Here, we take advantage of this rich information at a single-trial level to decode stimulus-related signals in two event-related potential (ERP) studies. Our method models the statistical distribution of the voltage topographies with a Gaussian Mixture Model (GMM), which reduces the dataset to a number of representative voltage topographies. The degree of presence of these topographies across trials at specific latencies is then used to classify experimental conditions. We tested the algorithm using a cross-validation procedure in two independent EEG datasets. In the first ERP study, we classified left- versus right-hemifield checkerboard stimuli for upper and lower visual hemifields. In a second ERP study, when functional differences cannot be assumed, we classified initial versus repeated presentations of visual objects. With minimal a priori information, the GMM model provides neurophysiologically interpretable features - vis à vis voltage topographies - as well as dynamic information about brain function. This method can in principle be applied to any ERP dataset testing the functional relevance of specific time periods for stimulus processing, the predictability of subject's behavior and cognitive states, and the discrimination between healthy and clinical populations.

Glutathione precursor N-acetyl-cysteine modulates EEG synchronization in schizophrenia patients: a double-blind, randomized, placebo-controlled trial.

Glutathione (GSH) dysregulation at the gene, protein, and functional levels has been observed in schizophrenia patients. Together with disease-like anomalies in GSH deficit experimental models, it suggests that such redox dysregulation can play a critical role in altering neural connectivity and synchronization, and thus possibly causing schizophrenia symptoms. To determine whether increased GSH levels would modulate EEG synchronization, N-acetyl-cysteine (NAC), a glutathione precursor, was administered to patients in a randomized, double-blind, crossover protocol for 60 days, followed by placebo for another 60 days (or vice versa). We analyzed whole-head topography of the multivariate phase synchronization (MPS) for 128-channel resting-state EEGs that were recorded at the onset, at the point of crossover, and at the end of the protocol. In this proof of concept study, the treatment with NAC significantly increased MPS compared to placebo over the left parieto-temporal, the right temporal, and the bilateral prefrontal regions. These changes were robust both at the group and at the individual level. Although MPS increase was observed in the absence of clinical improvement at a group level, it correlated with individual change estimated by Liddle's disorganization scale. Therefore, significant changes in EEG synchronization induced by NAC administration may precede clinically detectable improvement, highlighting its possible utility as a biomarker of treatment efficacy. TRIAL REGISTRATION: ClinicalTrials.gov NCT01506765.