Spatiotemporal analysis of multichannel EEG: CARTOOL.

This paper describes methods to analyze the brain's electric fields recorded with multichannel Electroencephalogram (EEG) and demonstrates their implementation in the software CARTOOL. It focuses on the analysis of the spatial properties of these fields and on quantitative assessment of changes of field topographies across time, experimental conditions, or populations. Topographic analyses are advantageous because they are reference independents and thus render statistically unambiguous results. Neurophysiologically, differences in topography directly indicate changes in the configuration of the active neuronal sources in the brain. We describe global measures of field strength and field similarities, temporal segmentation based on topographic variations, topographic analysis in the frequency domain, topographic statistical analysis, and source imaging based on distributed inverse solutions. All analysis methods are implemented in a freely available academic software package called CARTOOL. Besides providing these analysis tools, CARTOOL is particularly designed to visualize the data and the analysis results using 3-dimensional display routines that allow rapid manipulation and animation of 3D images. CARTOOL therefore is a helpful tool for researchers as well as for clinicians to interpret multichannel EEG and evoked potentials in a global, comprehensive, and unambiguous way.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

Differential effects of GABAB receptor subtypes, {gamma}-hydroxybutyric Acid, and Baclofen on EEG activity and sleep regulation.

The role of GABA(B) receptors in sleep is still poorly understood. GHB (γ-hydroxybutyric acid) targets these receptors and is the only drug approved to treat the sleep disorder narcolepsy. GABA(B) receptors are obligate dimers comprised of the GABA(B2) subunit and either one of the two GABA(B1) subunit isoforms, GABA(B1a) and GABA(B1b). To better understand the role of GABA(B) receptors in sleep regulation, we performed electroencephalogram (EEG) recordings in mice devoid of functional GABA(B) receptors (1(-/-) and 2(-/-)) or lacking one of the subunit 1 isoforms (1a(-/-) and 1b(-/-)). The distribution of sleep over the day was profoundly altered in 1(-/-) and 2(-/-) mice, suggesting a role for GABA(B) receptors in the circadian organization of sleep. Several other sleep and EEG phenotypes pointed to a more prominent role for GABA(B1a) compared with the GABA(B1b) isoform. Moreover, we found that GABA(B1a) protects against the spontaneous seizure activity observed in 1(-/-) and 2(-/-) mice. We also evaluated the effects of the GHB-prodrug GBL (γ-butyrolactone) and of baclofen (BAC), a high-affinity GABA(B) receptor agonist. Both drugs induced a state distinct from physiological sleep that was not observed in 1(-/-) and 2(-/-) mice. Subsequent sleep was not affected by GBL whereas BAC was followed by a delayed hypersomnia even in 1(-/-) and 2(-/-) mice. The differential effects of GBL and BAC might be attributed to differences in GABA(B)-receptor affinity. These results also indicate that all GBL effects are mediated through GABA(B) receptors, although these receptors do not seem to be involved in mediating the BAC-induced hypersomnia.

EEG reactivity to pain in comatose patients: Importance of the stimulus type.

INTRODUCTION: Electroencephalogram (EEG) background reactivity is a potentially interesting outcome predictor in comatose patients, especially after cardiac arrest, but recent studies report only fair interrater reliability. Furthermore, there are no definite guidelines for its testing. We therefore investigated the EEG effect of standardized noxious stimuli in comatose patients not reactive to auditory stimuli. METHODS: In this prospective study we applied a protocol using three different painful stimuli (bilateral nipple pinching, pinprick at the nose base, finger-nail compression on each side), grouped in three distinct clusters with an alternated sequence, during EEG recordings in comatose patients. We only analyzed recordings showing any reactivity to pain. Fisher and χ2 tests were used as needed to assess contingency tables. RESULTS: Of 42 studies, 12 did not show any background reactivity, 2 presented SIRPIDs, and 2 had massive artefacts; we thus analyzed 26 EEGs recorded in 17 patients (4 women, 24%). Nipple pinching more frequently induced a change in EEG background activity (p<0.001), with a sensitivity of 97.4% for reactivity. Neither the order of the stimuli in the cluster (p=0.723), nor the cluster order (p=0.901) influenced the results. CONCLUSION: In this pilot study, bilateral, synchronous nipple pinching seems to be the most efficient method to test nociceptive EEG reactivity in comatose patients. This approach may enhance interrater reliability, but deserves confirmation in larger cohorts.

Robustness of mutual information to inter-subject variability for automatic artefact removal from EEG

The externally recorded electroencephalogram (EEG) is contaminated with signals that do not originate from the brain, collectively known as artefacts. Thus, EEG signals must be cleaned prior to any further analysis. In particular, if the EEG is to be used in online applications such as Brain-Computer Interfaces (BCIs) the removal of artefacts must be performed in an automatic manner. This paper investigates the robustness of Mutual Information based features to inter-subject variability for use in an automatic artefact removal system. The system is based on the separation of EEG recordings into independent components using a temporal ICA method, RADICAL, and the utilisation of a Support Vector Machine for classification of the components into EEG and artefact signals. High accuracy and robustness to inter-subject variability is achieved.

Bridging a yawning chasm: EEG investigations into the debate concerning the role of the human mirror neuron system in contagious yawning

Ongoing debate in the literature concerns whether there is a link between contagious yawning and the human mirror neuron system (hMNS). One way of examining this issue is with the use of the electroencephalogram (EEG) to measure changes in mu activation during the observation of yawns. Mu oscillations are seen in the alpha bandwidth of the EEG (8–12 Hz) over sensorimotor areas. Previous work has shown that mu suppression is a useful index of hMNS activation and is sensitive to individual differences in empathy. In two experiments, we presented participants with videos of either people yawning or control stimuli. We found greater mu suppression for yawns than for controls over right motor and premotor areas, particularly for those scoring higher on traits of empathy. In a third experiment, auditory recordings of yawns were compared against electronically scrambled versions of the same yawns. We observed greater mu suppression for yawns than for the controls over right lateral premotor areas. Again, these findings were driven by those scoring highly on empathy. The results from these experiments support the notion that the hMNS is involved in contagious yawning, emphasise the link between contagious yawning and empathy, and stress the importance of good control stimuli.

EEG activation differences in the pre-motor cortex and supplementary motor area between normal individuals with high and low traits of autism

The human mirror neuron system (hMNS) is believed to provide a basic mechanism for social cognition. Event-related desynchronization (ERD) in alpha (8–12 Hz) and low beta band (12–20 Hz) over sensori-motor cortex has been suggested to index mirror neurons' activity. We tested whether autistic traits revealed by high and low scores on the Autistic Quotient (AQ) in the normal population are linked to variations in the electroencephalogram (EEG) over motor, pre-motor cortex and supplementary motor area (SMA) during action observation. Results revealed that in the low AQ group, the pre-motor cortex and SMA were more active during hand action than static hand observation whereas in the high AQ group the same areas were active both during static and hand action observation. In fact participants with high traits of autism showed greater low beta ERD while observing the static hand than those with low traits and this low beta ERD was not significantly different when they watched hand actions. Over primary motor cortex, the classical alpha and low beta ERD during hand actions relative to static hand observation was found across all participants. These findings suggest that the observation–execution matching system works differently according to the degree of autism traits in the normal population and that this is differentiated in terms of the EEG according to scalp site and bandwidth.

Testing for significance of phase synchronisation dynamics in the EEG

A number of tests exist to check for statistical significance of phase synchronisation within the Electroencephalogram (EEG); however, the majority suffer from a lack of generality and applicability. They may also fail to account for temporal dynamics in the phase synchronisation, regarding synchronisation as a constant state instead of a dynamical process. Therefore, a novel test is developed for identifying the statistical significance of phase synchronisation based upon a combination of work characterising temporal dynamics of multivariate time-series and Markov modelling. We show how this method is better able to assess the significance of phase synchronisation than a range of commonly used significance tests. We also show how the method may be applied to identify and classify significantly different phase synchronisation dynamics in both univariate and multivariate datasets.

Ketamine, propofol, and the EEG: A neural field analysis of HCN1-mediated interactions

Ketamine and propofol are two well-known, powerful anesthetic agents, yet at first sight this appears to be their only commonality. Ketamine is a dissociative anesthetic agent, whose main mechanism of action is considered to be N-methyl-D-aspartate (NMDA) antagonism; whereas propofol is a general anesthetic agent, which is assumed to primarily potentiate currents gated by γ-aminobutyric acid type A (GABAA) receptors. However, several experimental observations suggest a closer relationship. First, the effect of ketamine on the electroencephalogram (EEG) is markedly changed in the presence of propofol: on its own ketamine increases θ (4–8 Hz) and decreases α (8–13 Hz) oscillations, whereas ketamine induces a significant shift to beta band frequencies (13–30 Hz) in the presence of propofol. Second, both ketamine and propofol cause inhibition of the inward pacemaker current Ih, by binding to the corresponding hyperpolarization-activated cyclic nucleotide-gated potassium channel 1 (HCN1) subunit. The resulting effect is a hyperpolarization of the neuron’s resting membrane potential. Third, the ability of both ketamine and propofol to induce hypnosis is reduced in HCN1-knockout mice. Here we show that one can theoretically understand the observed spectral changes of the EEG based on HCN1-mediated hyperpolarizations alone, without involving the supposed main mechanisms of action of these drugs through NMDA and GABAA, respectively. On the basis of our successful EEG model we conclude that ketamine and propofol should be antagonistic to each other in their interaction at HCN1 subunits. Such a prediction is in accord with the results of clinical experiment in which it is found that ketamine and propofol interact in an infra-additive manner with respect to the endpoints of hypnosis and immobility.

Modeling the effects of anesthesia on the electroencephalogram

Changes to the electroencephalogram (EEG) observed during general anesthesia are modeled with a physiological mean field theory of electrocortical activity. To this end a parametrization of the postsynaptic impulse response is introduced which takes into account pharmacological effects of anesthetic agents on neuronal ligand-gated ionic channels. Parameter sets for this improved theory are then identified which respect known anatomical constraints and predict mean firing rates and power spectra typically encountered in human subjects. Through parallelized simulations of the eight nonlinear, two-dimensional partial differential equations on a grid representing an entire human cortex, it is demonstrated that linear approximations are sufficient for the prediction of a range of quantitative EEG variables. More than 70 000 plausible parameter sets are finally selected and subjected to a simulated induction with the stereotypical inhaled general anesthetic isoflurane. Thereby 86 parameter sets are identified that exhibit a strong “biphasic” rise in total power, a feature often observed in experiments. A sensitivity study suggests that this “biphasic” behavior is distinguishable even at low agent concentrations. Finally, our results are briefly compared with previous work by other groups and an outlook on future fits to experimental data is provided.

Understanding the effects of anesthetic agents on the EEG through neural field theory

Anesthetic and analgesic agents act through a diverse range of pharmacological mechanisms. Existing empirical data clearly shows that such "microscopic" pharmacological diversity is reflected in their "macroscopic" effects on the human electroencephalogram (EEG). Based on a detailed mesoscopic neural field model we theoretically posit that anesthetic induced EEG activity is due to selective parametric changes in synaptic efficacy and dynamics. Specifically, on the basis of physiologically constrained modeling, it is speculated that the selective modification of inhibitory or excitatory synaptic activity may differentially effect the EEG spectrum. Such results emphasize the importance of neural field theories of brain electrical activity for elucidating the principles whereby pharmacological agents effect the EEG. Such insights will contribute to improved methods for monitoring depth of anesthesia using the EEG.

Novel single trial movement classification based on temporal dynamics of EEG

Various complex oscillatory processes are involved in the generation of the motor command. The temporal dynamics of these processes were studied for movement detection from single trial electroencephalogram (EEG). Autocorrelation analysis was performed on the EEG signals to find robust markers of movement detection. The evolution of the autocorrelation function was characterised via the relaxation time of the autocorrelation by exponential curve fitting. It was observed that the decay constant of the exponential curve increased during movement, indicating that the autocorrelation function decays slowly during motor execution. Significant differences were observed between movement and no moment tasks. Additionally, a linear discriminant analysis (LDA) classifier was used to identify movement trials with a peak accuracy of 74%.

Automated artifact removal from the electroencephalogram; a comparative study

Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.

Desenvolvimento pós-natal do EEG em cães normais: avaliação visual qualitativa e quantitativa até 45 dias de vida

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

EEG signal classification for epilepsy diagnosis via optimum path forest - A systematic assessment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)