Neuronal oscillations in the EEG under varying cognitive load: A comparative study between slow waves and faster oscillations (original) (raw)
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Identifying robust and sensitive frequency bands for interrogating neural oscillations
Recent years have seen an explosion of interest in using neural oscillations to characterize the mechanisms supporting cognition and emotion. Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands. Some investigators use broad bands originally defined by prominent surface features of the spectrum. Others rely on narrower bands originally defined by spectral factor analysis (SFA). Presently, the robustness and sensitivity of these competing band definitions remains unclear. Here, a Monte Carlo-based SFA strategy was used to decompose the tonic (“resting” or “spontaneous”) electroencephalogram (EEG) into five bands: delta (1–5 Hz), alpha-low (6–9 Hz), alpha-high (10–11 Hz), beta (12–19 Hz), and gamma (N21 Hz). This pattern was consistent across SFA methods, artifact correction/rejection procedures, scalp regions, and samples. Subsequent analyses revealed that SFA failed to deliver enhanced sensitivity; narrow alpha sub-bands proved no more sensitive than the classical broadband to individual differences in temperament or mean differences in task-induced activation. Other analyses suggested that residual ocular and muscular artifact was the dominant source of activity during quiescence in the delta and gamma bands. This was observed following threshold-based artifact rejection or independent component analysis (ICA)- based artifact correction, indicating that such procedures do not necessarily confer adequate protection. Collectively, these findings highlight the limitations of several commonly used EEG procedures and underscore the necessity of routinely performing exploratory data analyses, particularly data visualization, prior to hypothesis testing. They also suggest the potential benefits of using techniques other than SFA for interrogating high-dimensional EEG datasets in the frequency or time–frequency (event-related spectral perturbation, event-related synchronization/desynchronization) domains. KEY WORDS: principal components analysis (PCA); exploratory factor analysis (EFA); blind source separation (BSS); resting neural activity; resting EEG; frontal alpha asymmetry; frontal EEG asymmetry.
Brain Topography, 2013
When dealing with electroencephalograms (EEGs) recorded under resting conditions, periods of lowvoltage activity might indicate drowsiness, but mental activation as well. Thus, low-voltage EEG retrieves a notorious source of confusion. The simultaneous occurrence of drowsiness related slow horizontal eye movements (SEM) allow to assign low-voltage EEG-activity to a brain state of reduced vigilance instead of mental activation. The aim of this study was to investigate, whether under resting conditions with eyes closed low-voltage EEG with SEM (B1?) and without SEM (B1-) differ in spectral and spatial distribution of EEG-activity. EEGs of 35 healthy subjects where analyzed, each containing at least 10 s of low-voltage EEG recorded during a calculation task (calc, as control condition), as well as 10 s of each B1-and B1?, recorded during following about 20 min of rest. Using standardized, low resolution brain electromagnetic tomography, cortical current density was computed in four individually adapted frequency bands (delta, theta, alpha, beta) for calc, B1-and B1?. Paired test comparison of cortical current densities revealed significant differences for B1-compared to B1?. In detail, B1-exhibited lower delta-and theta band activity, especially in the cingulate-and adjacent medial portions of the frontal, parietal and occipital cortices, as well as higher beta band activity in temporal cortices. Similar results where found in calc versus B1?. These findings support the association of B1-to a higher level of vigilance compared to B1?, thus justifying the separation of low-voltage EEG-activity by means of SEM.
PloS one, 2015
The observation of highly variable sets of association neocortical areas across individuals, containing the estimated generators of Slow Potentials (SPs) and beta oscillations, lead to the persistence in individual analyses. This brought to notice an unexpected within individual topographic similarity between task conditions, despite our original interest in task-related differences. A recent related work explored the quantification of the similarity in beta topography between largely differing tasks. In this article, we used Independent Component Analysis (ICA) for the decomposition of beta activity from a visual attention task, and compared it with quiet resting, recorded by 128-channel EEG in 62 subjects. We statistically tested whether each ICA component obtained in one condition could be explained by a linear regression model based on the topographic patterns from the other condition, in each individual. Results were coherent with the previous report, showing a high topographic...
Co-modulatory spectral changes in independent brain processes are correlated with task performance
NeuroImage, 2012
This study investigates the independent modulators that mediate the power spectra of electrophysiological processes, measured by electroencephalogram (EEG), in a sustained-attention experiment. EEG and behavioral data were collected during 1-2 hour virtual-reality based driving experiments in which subjects were instructed to maintain their cruising position and compensate for randomly induced drift using the steering wheel. Independent component analysis (ICA) applied to 30-channel EEG data separated the recorded EEG signals into a sum of maximally temporally independent components (ICs) for each of 30 subjects. Logarithmic spectra of resultant IC activities were then decomposed by principal component analysis, followed by ICA, to find spectrally fixed and temporally independent modulators (IM). Across subjects, the spectral ICA consistently found four performance-related independent modulators: delta, delta-theta, alpha, and beta modulators that multiplicatively affected the spectra of spatially distinct IC processes when the participants experienced waves of alternating alertness and drowsiness during long-hour simulated driving. The activation of the delta-theta modulator increased monotonically as subjects' task performances decreased. Furthermore, the time courses of the theta-beta modulator were highly correlated with concurrent changes in driving errors across subjects (r = 0.77 ± 0.13).
Short-term variability in EEG frequency analysis
Electroencephalography and Clinical Neurophysiology, 1988
Knowledge of short-term EEG variability in computerized analysis is important before interpreting spectral EEGs or assessing changes that may be due to inherent variability and not necessarily related to a task (e.g., listening to a story), therapy or changes in underlying disease. Eighty to 120 sec of 14-channel, edited, bipolar EEG were recorded in normal subjects and analyzed using an FFT. Absolute and relative power in 5 standard frequency bands, and median and peak power frequencies were obtained for each 4 sec epoch, and the mean and standard deviation calculated for each parameter. The average variation of the mean power, absolute and relative, in the frequency bands was less than 10% although some parameters varied by up to 50% in an individual subject. Median and peak power had the least variability, about 3%. Changes in total power correlated positively with relative alpha power, but negatively or not at all with the other relative power measures. This suggests that interpretation of relative measures of delta, theta and beta in individual spectra may be dependent on total power or absolute alpha power. In addition, mathematical transformations were necessary to normalize the epoch by epoch data, suggesting that the mean and standard deviation of data from a series of epochs may not have maximal value unless a transformation is used. These results also indicate that caution is needed in interpreting changes in EEG frequency analysis data that are of the same magnitude as spontaneous EEG variability.
Brain Research, 2013
The default mode network (DMN) is characterised by coherent very low frequency (VLF) neural oscillations in the resting brain. The attenuation of this activity has been demonstrated following the transition from rest to performance of a broad range of cognitive goal-directed tasks. Whether the activity of resting state VLF oscillations is attenuated during non-cognitive goal-directed tasks such as waiting for rewarding outcomes is not known. This study examined the VLF EEG power from resting to performance of attention demanding task and two types of goal-directed waiting tasks. The association between the attenuation of VLF EEG power and Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms was examined. Direct current EEG (DC-EEG) data was collected from 32 healthy young adults (half high and half low ADHD symptom scorers) during (i) a rest state, (ii) while performing a cognitive demanding reaction time task (2CRT), and (iii) while undertaking each of two different goal-directed waiting conditions: "forced-to-wait (FW)" and "choose-to-wait (CW)" tasks. The spatial distribution of VLF EEG power across scalp was similar to that seen in previous resting VLF EEG studies. Significant rest-to-task attenuation of VLF EEG power occurred during the 2CRT and the CW task, but not during the FW task. The association between self-ratings of ADHD symptoms and waiting-induced attenuation was not significant. This study suggests VLF EEG power attenuation that occurs following rest to task transition is not simply determined by changes in cognitive load. The goal-directed nature of a task, its motivated nature and/or the involvement of effortful attention may also contribute. Future studies should explore the attenuation of resting state VLF oscillations during waiting and impulsive choice.
Blind Source Separation Can Recover Systematically Distributed Neuronal Sources From resting EEG
Eurasip Proceedings of the Second International …
Blind source separation algorithms have been increasingly applied to electroencephalographic (EEG) and magnetoencephalographic (MEG) signals from the human brain. Second-order blind identification (SOBI) [1] is one of the emerging algorithms which enable the extraction of functionally distinct, neuro-physiologically, and anatomically meaningful components. SOBI's ability to extract activity associated with a variety of brain sources during visual, auditory, and somatosensory stimulation has been well documented [2]-[6] . Here we demonstrate that SOBI is able to extract a set of neuronal components distributed within the visual, and somatosensory systems, as well as the frontal cortices, from resting EEG data obtained in the absence of explicit sensory stimulation or overt behavioral responses.
Interindividual variability in EEG correlates of attention and limits of functional mapping
International Journal of Psychophysiology, 2007
In this study, we analyzed the EEG oscillatory activity induced during a simple visual task, in search of spectral correlate(s) of attention. This task has been previously analyzed by conventional event-related potential (ERP) computation, and Slow Potentials (SPs) were seen to be highly variable across subjects in topography and generators [Basile LF, Brunetti EP, Pereira JF Jr, Ballester G, Amaro E Jr, Anghinah R, Ribeiro P, Piedade R, Gattaz WF. (2006) Complex slow potential generators in a simplified attention paradigm. Int J Psychophysiol. 61(2):149-57]. We obtained 124-channel EEG recordings from 12 individuals and computed latency-corrected peak averaging in oscillatory bursts. We used currentdensity reconstruction to model the generators of attention-related activity that would not be seen in ERPs, which are restricted to stimulus-locked activity. We intended to compare a possibly found spectral correlate of attention, in topographic variability, with stimulus-related activity. The main results were (1) the detection of two bands of attention-induced beta range oscillations (around 25 and 21 Hz), whose scalp topography and current density cortical distribution were complex multi-focal, and highly variable across subjects (topographic dispersion significantly higher than sensory-related visual theta induced band-power), including prefrontal and posterior cortical areas. Most interesting, however, was the observation that (2) the generators of task-induced oscillations are largely the same individual-specific sets of cortical areas active during the pre-stimulus baseline. We concluded that attention-related electrical cortical activity is highly individual-specific, and possibly, to a great extent already established during mere resting wakefulness. We discuss the critical implications of those results, in combination with results from other methods that present individual data, to functional mapping of cortical association areas.
Front. Hum. Neurosci. , 2019
In the study of human cognitive activity using electroencephalogram (EEG), the brain dynamics parameters and characteristics play a crucial role. They allow to investigate the changes in functionality depending on the environment and task performance process, and also to access the intensity of the brain activity in various locations of the cortex and its dependencies. Usually, the dynamics of activation of different brain areas during the cognitive tasks are being studied by spectral analysis based on power spectral density (PSD) estimation, and coherence analysis, which are de facto standard tools in quantitative characterization of brain activity. PSD and coherence reflect the strength of oscillations and similarity of the emergence of these oscillations in the brain, respectively, while the concept of stability of brain activity over time is not well defined and less formalized. We propose to employ the detrended fluctuation analysis (DFA) as a measure of the EEG persistence over time, and use the DFA scaling exponent as its quantitative characteristics. We applied DFA to the study of the changes in activation in brain dynamics during mental calculations and united it with PSD and coherence estimation. In the experiment, EEGs during resting state and mental serial subtraction from 36 subjects were recorded and analyzed in four frequency ranges: θ1 (4.1–5.8 Hz), θ2 (5.9–7.4 Hz), β1 (13–19.9 Hz), and β2 (20–25 Hz). PSD maps to access the intensity of cortex activation and coherence to quantify the connections between different brain areas were calculated, the distribution of DFA scaling exponent over the head surface was exploited to measure the time characteristics of the dynamics of brain activity. Obtained arrangements of DFA scaling exponent suggest that normal functioning of the brain is characterized by long-term temporal correlations in the cortex. Topographical distribution of the DFA scaling exponent was comparable for θ and β frequency bands, demonstrating the largest values of DFA scaling exponent during cognitive activation. The study shows that the long-term temporal correlations evaluated by DFA can be of great interest for diagnosis of the variety of brain dysfunctions of different etiology in the future.