EEG correlates of hypnotic susceptibility based upon fast Fourier power spectral analysis

The purpose of the present study was to determine if there were differences between high and low hypnotic susceptible subjects based upon fast Fourier power spectral analysis of the EEG recorded both before and during hypnotic tasks from frontal-temporal and occipital-parietal locations. Significant differences were obtained based upon EEG recording electrode location, EEG frequency within six different frequency domains, and hypnotic tasks. However, no main effect differences were obtained based upon hypnotic susceptibility. In contrast to some evoked potential studies in which a few differences have been obtained based on hypnotic susceptibility the lack of any EEG differences in this study even when positive and negative hallucination tasks were employed may have implications for the role of the neocortex in mediating hypnotic phenomena.     

Online Classification of Multiple Motor Imagery Tasks Using Filter Bank Based Maximum-a-Posteriori Common Spatial Pattern Filters

ObjectiveThe main objective of this paper is to propose a novel technique, called filter bank maximum a-posteriori common spatial pattern (FB-MAP-CSP) algorithm, for online classification of multiple motor imagery activities using electroencephalography (EEG) signals. The proposed technique addresses the overfitting issue of CSP in addition to utilizing the spectral information of EEG signals inside the framework of filter banks while extending it to more than two conditions.Materials and methodsThe classification of motor imagery signals is based upon the detection of event-related de-synchronization (ERD) phenomena in the μ and β rhythms of EEG signals. Accordingly, two modifications in the existing MAP-CSP technique are presented: (i) The (pre-processed) EEG signals are spectrally filtered by a bank of filters lying in the μ and β brainwave frequency range, (ii) the framework of MAP-CSP is extended to deal with multiple (more than two) motor imagery tasks classification and the spatial filters thus obtained are calculated for each sub-band, separately. Subsequently, the most imperative features over all sub-bands are selected and un-regularized linear discriminant analysis is employed for classification of multiple motor imagery tasks.ResultsPublicly available dataset (BCI Competition IV Dataset I) is used to validate the proposed method i.e. FB-MAP-CSP. The results show that the proposed method yields superior classification results, in addition to be computationally more efficient in the case of online implementation, as compared to the conventional CSP based techniques and its variants for multiclass motor imagery classification.ConclusionThe proposed FB-MAP-CSP algorithm is found to be a potential / superior method for classifying multi-condition motor imagery EEG signals in comparison to FBCSP based techniques.     

Human electrophysiological and cognitive effects of exposure to ELF magnetic and ELF modulated RF and microwave fields: a review of recent studies

The investigation of weak (<500 microT), extremely low frequency (ELF, 0-300 Hz) magnetic field (MF) exposure upon human cognition and electrophysiology has yielded incomplete and contradictory evidence that MFs interact with human biology. This may be due to the small number of studies undertaken examining ELF MF effects upon the human electroencephalogram (EEG), and the associated analysis of evoked related potentials (ERPs). Relatively few studies have examined how MF exposure may affect cognitive and perceptual processing in human subjects. The introduction of this review considers some of the recent studies of ELF MF exposure upon the EEG, ERPs and cognitive and perceptual tasks. We also consider some of the confounding factors within current human MF studies and suggest some new strategies for further experimentation.     

A digital signal processing system for EEG frequency analysis

This paper describes the digital signal processing work of a research project for studying children's cognitive processes by analyzing EEG signals during school-related tasks. The EEG being analyzed involves two homologous channels (left and right parietal area), and is recorded on magnetic tapes. The objective of the analysis is to determine if, by examining the alpha band of the ongoing EEG, different school tasks and correct vs incorrect responses can be detected. Analysis of alpha-band calls for the determination of signal power in the 7-12 Hz frequency band (adjusted for the age of the subjects) for each channel as well as correlation between the channels. A digital signal processing scheme implemented on an Apple II microcomputer was developed for such an analysis. The results obtained are discussed.     

Changes in human EEG alpha activity following exposure to two different pulsed magnetic field sequences

The present study investigates the effects of a weak (+/-200 microT(pk)), pulsed, extremely low frequency magnetic field (ELF MF) upon the human electroencephalogram (EEG). We have previously determined that exposure to pulsed ELF MFs can affect the EEG, notably the alpha frequency (8-13 Hz) over the occipital-parietal region of the scalp. In the present study, subjects (n = 32) were exposed to two different pulsed MF sequences (1 and 2, used previously) that differed in presentation rate, in order to examine the effects upon the alpha frequency of the human EEG. Results suggest that compared to sham exposure, alpha activity was lowered over the occipital-parietal regions of the brain during exposure to Sequence 1, while alpha activity over the same regions was higher after Sequence 2 exposure. These effects occurred after approximately 5 min of pulsed MF exposure. The results also suggest that a previous exposure to the pulsed MF sequence determined subjects' responses in the present experiment. This study supports our previous observation of EEG changes after 5 min pulsed ELF MF exposure. The results of this study are also consistent with existing EEG experiments of ELF MF and mobile phone effects upon the brain.     

Localizing Movement-Related Primary Sensorimotor Cortices with Multi-Band EEG Frequency Changes and Functional MRI

Electroencephalographic (EEG) oscillations in multiple frequency bands can be observed during functional activity of the cerebral cortex. An important question is whether activity of focal areas of cortex, such as during finger movements, is tracked by focal oscillatory EEG changes. Although a number of studies have compared EEG changes to functional MRI hemodynamic responses, we can find no previous research that relates the fMRI hemodynamic activity to localization of the multiple EEG frequency changes observed in motor tasks. In the present study, five participants performed similar thumb and finger movement tasks in parallel EEG and functional MRI studies. We examined changes in five frequency bands (from 5–120 Hz) and localized them using 256 dense-array EEG (dEEG) recordings and high-resolution individual head models. These localizations were compared with fMRI localizations in the same participants. Results showed that beta-band (14–30 Hz) desynchronizations (power decreases) were the most robust effects, appearing in all individuals, consistently localized to the hand region of the primary motor cortex, and consistently aligned with fMRI localizations.     

The reflection of different aspects of brain activation in the electroencephalogram: Quantitative electroencephalography of the states of rest with the eyes open and closed

The rest states with the eyes open (RSEO) and closed (RSEC) were subjected to quantitative EEG study as states similar in the pattern of mental activity and subjective assessments but different in the EEG pattern. The mean values of the spectral power and EEG coherence function were compared in 74 subjects for the following bands: Δ, ?, α₁, α₂, β₁, β₂, and γ. Upon the transition from the RSEC to the RSEO, the EEG local power significantly decreased over the whole cortex for the α, ?, and β bands. A simultaneous decrease in the EEG power in all the bands (including β and γ) was most pronounced (as judged by relative changes and tests of significance of difference) in the parietooccipital derivations immediately related to the cortical zones where an increase in the neuronal activity upon opening the eyes is most probable. A significant increase in the EEG power was observed only for the γ band in frontal derivations F ₃ and F ₄. Significant differences in the mean EEG coherence in the RSEO-RSEC comparison were present in many derivation pairs, especially in the α₂, β₁, β₂, and γ bands. For each of these bands, the number of differences determined on the basis of Fisher test was more than 70% of the maximum possible number. In the overwhelming majority of cases, the coherence was lower in the RSEO; however, in the caudal cortical zones, a higher coherence in the α₁, ?, and Δ bands in the RSEO was rather typical. The results confirmed that the two states under study differ in a number of averaged EEG parameters with high statistical significance and may be used as reference states during performance of tasks with the eyes open and closed, respectively. The differences between the RSEC and the RSEO may be caused by the fact that the RSEC is a functional state oriented predominantly to the analysis of internal information (internally oriented), and the RSEO, predominantly to the analysis of information coming from the outside (externally oriented). The pattern of the observed EEG differences points to a combination of effects both localized in the visual zone and reflecting changes in the network cortical activity, i.e., simultaneous, although nonuniform, changes over all the main zones of the cortex. Comparison of the results with published estimations of differences in the local cerebral blood flow (ICBF) between the RSEO and the RSEC shows that increase in the ICBF may be associated with a local decrease in the EEG spectral power in any frequency band, including the high-frequency β and γ bands, or several frequency bands simultaneously.     

EEG correlates of geno-phenotypical features of the brain development in children of the native and newcomers' population of the Russian North-East

Traditional and original methods of EEG analysis were used to study the brain electrical activity maturation in 156 children and adolescents from 7 to 17 years old who represented the native (Koryaks and Evenks) and newcomers' populations living in severe climatic and geographic conditions of the Russian North-East. New data revealing age-, sex- and ethnic-related features in quantitative EEG parameters are presented. Markers are obtained that characterize alterations in the structure of interaction between different EEG rhythms. The results demonstrate age-dependent transformation of this structure separated in time for both different cortical areas and different EEG frequency bands. These alterations show time lag from 2 to 3 years in children of native population compared to the newcomers. The revealed differences are assumed to reflect geno-phenotypical features of morpho-functional CNS development in children of the native and newcomers' population that depend on strong adaptation tension for extreme environmental conditions.     

Characterizing Alzheimer’s Disease Severity via Resting-Awake EEG Amplitude Modulation Analysis

Changes in electroencephalography (EEG) amplitude modulations have recently been linked with early-stage Alzheimer’s disease (AD). Existing tools available to perform such analysis (e.g., detrended fluctuation analysis), however, provide limited gains in discriminability power over traditional spectral based EEG analysis. In this paper, we explore the use of an innovative EEG amplitude modulation analysis technique based on spectro-temporal signal processing. More specifically, full-band EEG signals are first decomposed into the five well-known frequency bands and the envelopes are then extracted via a Hilbert transform. Each of the five envelopes are further decomposed into four so-called modulation bands, which were chosen to coincide with the delta, theta, alpha and beta frequency bands. Experiments on a resting-awake EEG dataset collected from 76 participants (27 healthy controls, 27 diagnosed with mild-AD, and 22 with moderate-AD) showed significant differences in amplitude modulations between the three groups. Most notably, i) delta modulation of the beta frequency band disappeared with an increase in disease severity (from mild to moderate AD), ii) delta modulation of the theta band appeared with an increase in severity, and iii) delta modulation of the beta frequency band showed to be a reliable discriminant feature between healthy controls and mild-AD patients. Taken together, it is hoped that the developed tool can be used to assist clinicians not only with early detection of Alzheimer’s disease, but also to monitor its progression.     

Decoding hand movement velocity from electroencephalogram signals during a drawing task

Background  

Decoding neural activities associated with limb movements is the key of motor prosthesis control. So far, most of these studies have been based on invasive approaches. Nevertheless, a few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG) and electroencephalogram (EEG). Regarding these EEG studies, center-out reaching tasks have been employed. Yet whether hand velocity can be decoded using EEG recorded during a self-routed drawing task is unclear.     

Differences in baseline EEG measures for ADD and Normally Achieving preadolescent males

This study was designed as a replication of previous studies describing dynamic EEG differences between behaviorally dissimilar groups. This study is intended as a reference point from which other researchers can continue to establish the EEG correlates of on-task behavior. Eight Attention Deficit Disordered (ADD) children and eight Normally Achieving Controls (NAC) were assessed using dynamic EEG measures. Results are reported for the tasks of baseline (eyes open), reading, and drawing as recorded from FZ, CZ, PZ, C3, and C4. Significant amplitude differences between the groups were demonstrated in the theta band (4–8 hertz) during all tasks and for all sites. Amplitude differences in the beta band (12–20 hertz) were negligible. Differences between groups expressed as a ratio of theta/beta revealed significant differences mainly in the parietal region for on-task conditions. These results are discussed in relation to EEG neuro-feedback training paradigms and the importance of establishing normative on-task EEG values.The authors wish to acknowledge contributions of Hazel Dean school administration and students. Also, thanks are offered to Allan Pankratz for his assistance with graphics.     

Identifying object categories from event-related EEG: toward decoding of conceptual representations

Multivariate pattern analysis is a technique that allows the decoding of conceptual information such as the semantic category of a perceived object from neuroimaging data. Impressive single-trial classification results have been reported in studies that used fMRI. Here, we investigate the possibility to identify conceptual representations from event-related EEG based on the presentation of an object in different modalities: its spoken name, its visual representation and its written name. We used Bayesian logistic regression with a multivariate Laplace prior for classification. Marked differences in classification performance were observed for the tested modalities. Highest accuracies (89% correctly classified trials) were attained when classifying object drawings. In auditory and orthographical modalities, results were lower though still significant for some subjects. The employed classification method allowed for a precise temporal localization of the features that contributed to the performance of the classifier for three modalities. These findings could help to further understand the mechanisms underlying conceptual representations. The study also provides a first step towards the use of concept decoding in the context of real-time brain-computer interface applications.     

Cognitive brain–Computer interface and probable aspects of its practical application

A new type of brain-computer interface was elaborated. It considers a variety of brain activity parameters to determine the type of mental operation being performed at the moment. The corresponding algorithm previously developed in the lab was modified for real-time application. The possibility of interface application for cognitive skills training was investigated. In the proposed paradigm, as soon as the EEG spectral pattern was adequate for the current task, some clue to the solution was presented. As we supposed, such positive biofeedback should facilitate memorization of the current brain state. After just one learning session, the differences in EEG spectra, corresponding to two types of tasks, were concentrated in more narrow frequency ranges. It indicates a decrease in mental effort. Moreover, the majority of subjects succeeded in solving the tasks faster, which is evidence of increased efficiency. The developed interface could be used for the new type of training, based on objective features of brain activity.     

EEG-MEG Integration Enhances the Characterization of Functional and Effective Connectivity in the Resting State Network

At the sensor level many aspects, such as spectral power, functional and effective connectivity as well as relative-power-ratio ratio (RPR) and spatial resolution have been comprehensively investigated through both electroencephalography (EEG) and magnetoencephalography (MEG). Despite this, differences between both modalities have not yet been systematically studied by direct comparison. It remains an open question as to whether the integration of EEG and MEG data would improve the information obtained from the above mentioned parameters. Here, EEG (64-channel system) and MEG (275 sensor system) were recorded simultaneously in conditions with eyes open (EO) and eyes closed (EC) in 29 healthy adults. Spectral power, functional and effective connectivity, RPR, and spatial resolution were analyzed at five different frequency bands (delta, theta, alpha, beta and gamma). Networks of functional and effective connectivity were described using a spatial filter approach called the dynamic imaging of coherent sources (DICS) followed by the renormalized partial directed coherence (RPDC). Absolute mean power at the sensor level was significantly higher in EEG than in MEG data in both EO and EC conditions. At the source level, there was a trend towards a better performance of the combined EEG+MEG analysis compared with separate EEG or MEG analyses for the source mean power, functional correlation, effective connectivity for both EO and EC. The network of coherent sources and the spatial resolution were similar for both the EEG and MEG data if they were analyzed separately. Results indicate that the combined approach has several advantages over the separate analyses of both EEG and MEG. Moreover, by a direct comparison of EEG and MEG, EEG was characterized by significantly higher values in all measured parameters in both sensor and source level. All the above conclusions are specific to the resting state task and the specific analysis used in this study to have general conclusion multi-center studies would be helpful.     

Topographic Electrophysiological Signatures of fMRI Resting State Networks

Background

fMRI Resting State Networks (RSNs) have gained importance in the present fMRI literature. Although their functional role is unquestioned and their physiological origin is nowadays widely accepted, little is known about their relationship to neuronal activity. The combined recording of EEG and fMRI allows the temporal correlation between fluctuations of the RSNs and the dynamics of EEG spectral amplitudes. So far, only relationships between several EEG frequency bands and some RSNs could be demonstrated, but no study accounted for the spatial distribution of frequency domain EEG.

Methodology/Principal Findings

In the present study we report on the topographic association of EEG spectral fluctuations and RSN dynamics using EEG covariance mapping. All RSNs displayed significant covariance maps across a broad EEG frequency range. Cluster analysis of the found covariance maps revealed the common standard EEG frequency bands. We found significant differences between covariance maps of the different RSNs and these differences depended on the frequency band.

Conclusions/Significance

Our data supports the physiological and neuronal origin of the RSNs and substantiates the assumption that the standard EEG frequency bands and their topographies can be seen as electrophysiological signatures of underlying distributed neuronal networks.     

Towards a novel monitor of intraoperative awareness: selecting paradigm settings for a movement-based brain-computer interface

During 0.1-0.2% of operations with general anesthesia, patients become aware during surgery. Unfortunately, pharmacologically paralyzed patients cannot seek attention by moving. Their attempted movements may however induce detectable EEG changes over the motor cortex. Here, methods from the area of movement-based brain-computer interfacing are proposed as a novel direction in anesthesia monitoring. Optimal settings for development of such a paradigm are studied to allow for a clinically feasible system. A classifier was trained on recorded EEG data of ten healthy non-anesthetized participants executing 3-second movement tasks. Extensive analysis was performed on this data to obtain an optimal EEG channel set and optimal features for use in a movement detection paradigm. EEG during movement could be distinguished from EEG during non-movement with very high accuracy. After a short calibration session, an average classification rate of 92% was obtained using nine EEG channels over the motor cortex, combined movement and post-movement signals, a frequency resolution of 4 Hz and a frequency range of 8-24 Hz. Using Monte Carlo simulation and a simple decision making paradigm, this translated into a probability of 99% of true positive movement detection within the first two and a half minutes after movement onset. A very low mean false positive rate of <0.01% was obtained. The current results corroborate the feasibility of detecting movement-related EEG signals, bearing in mind the clinical demands for use during surgery. Based on these results further clinical testing can be initiated.     

小波和主分量分析方法研究思维脑电

研究自发脑电和思维活动的关系．利用小波和主分量分析结合的WPCA算法对不同思维任务记录的六导脑电进行处理,并对思维特征的频谱能量和变化率等多指标进行综合分析和计算。结果表明WPCA算法不仅可以实现噪声的去除,而且能提高主分量的贡献率,降低输入矢量的维数。对脑电主分量的分析揭示了脑电与思维个体、思维种类、复杂度以及注意力的联系,思维任务的神经网络分类结果验证了WPCA方法研究脑电和思维的有效性,为进一步理解认知和思维过程,实现对思维的定位和分类提供了依据。     

Method for Simultaneous fMRI/EEG Data Collection during a Focused Attention Suggestion for Differential Thermal Sensation

In the present work, we demonstrate a method for concurrent collection of EEG/fMRI data. In our setup, EEG data are collected using a high-density 256-channel sensor net. The EEG amplifier itself is contained in a field isolation containment system (FICS), and MRI clock signals are synchronized with EEG data collection for subsequent MR artifact characterization and removal. We demonstrate this method first for resting state data collection. Thereafter, we demonstrate a protocol for EEG/fMRI data recording, while subjects listen to a tape asking them to visualize that their left hand is immersed in a cold-water bath and referred to, here, as the cold glove paradigm. Thermal differentials between each hand are measured throughout EEG/fMRI data collection using an MR compatible temperature sensor that we developed for this purpose. We collect cold glove EEG/fMRI data along with simultaneous differential hand temperature measurements both before and after hypnotic induction. Between pre and post sessions, single modality EEG data are collected during the hypnotic induction and depth assessment process. Our representative results demonstrate that significant changes in the EEG power spectrum can be measured during hypnotic induction, and that hand temperature changes during the cold glove paradigm can be detected rapidly using our MR compatible differential thermometry device.     

Changes in EEG coherence during tests for nonverbal (Figurative) creativity

Brain neurodynamics was studied by the EEG method during the performance of a task for figurative (or imaginative) creativity. The EEG was recorded in 19 standard derivations according to the international 10–20 system in 30 subjects. The following creative tasks were presented to subjects to involve them in the creative process: (Crl) thinking up and drawing an original picture; (Cr2) drawing a face, a house, and a clown in an original manner; (C1) drawing a picture from memory; and (C2) drawing geometric figures without any system. All the tasks had to be performed using a given set of geometric figures (a circle, semicircle, triangle, and rectangle). Statistical analysis of the EEG coherence function in these states for the frequency bands Δ, θ, α₁, α₂, β₁, β₂, and γ showed that the performance of creative and control tasks was associated with significant coherence changes in all the EEG frequency bands. As compared to the control tasks, performance of creative tasks caused an increase in the coherence of the α₁-and α₂ bands, more pronounced when creative tasks were compared with the second control task. In addition, the performance of the creative tasks (as compared to the control tasks) was accompanied by a decrease in the interhemispheric coherence of high-frequency rhythms (β₂ and γ) and an increase in the intrahemispheric coherence of these rhythms. The findings are compared to the results of previous EEG studies on creative activity.