Effects of electrode density and electrolyte spreading in dense array electroencephalographic recording

A tutorial on fitting joint models of M/EEG and behavior to understand cognition

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

Periodic and aperiodic contributions to theta-beta ratios across adulthood

The ratio of fronto-central theta (4-7 Hz) to beta oscillations (13-30 Hz), known as the theta-beta ratio, is negatively correlated with attentional control, reinforcement learning, executive function, and age. Although theta-beta ratios have been found to decrease with age in adolescents and young adults, theta has been found to increase with age in older adults. Moreover, age-related decrease in individual alpha peak frequency and flattening of the 1/f aperiodic component may artifactually inflate the association between theta-beta ratio and age. These factors lead to an incomplete understanding of how theta-beta ratio varies across the lifespan and the extent to which variation is due to a conflation of aperiodic and periodic activity. We conducted a partially preregistered analysis examining the cross-sectional associations between age and resting canonical fronto-central theta-beta ratio, individual alpha peak frequency, and aperiodic component (n = 268; age 36-84, M = 55.8, SD = 11.0). Age was negatively associated with theta-beta ratios, individual peak alpha frequencies, and the aperiodic exponent. The correlation between theta-beta ratios and age remained after controlling for individual peak alpha frequencies, but was nonsignificant when controlling for the aperiodic exponent. Aperiodic exponent fully mediated the relationship between theta-beta ratio and age, although beta remained significantly associated with age after controlling for theta, individual peak alpha, and aperiodic exponent. Results replicate previous observations and show age-related decreases in theta-beta ratios are not due to age-related decrease in individual peak alpha frequencies but primarily explained by flattening of the aperiodic component with age.

Multi-Center Evaluation of Gel-Based and Dry Multipin EEG Caps

Dry electrodes for electroencephalography (EEG) allow new fields of application, including telemedicine, mobile EEG, emergency EEG, and long-term repetitive measurements for research, neurofeedback, or brain-computer interfaces. Different dry electrode technologies have been proposed and validated in comparison to conventional gel-based electrodes. Most previous studies have been performed at a single center and by single operators. We conducted a multi-center and multi-operator study validating multipin dry electrodes to study the reproducibility and generalizability of their performance in different environments and for different operators. Moreover, we aimed to study the interrelation of operator experience, preparation time, and wearing comfort on the EEG signal quality. EEG acquisitions using dry and gel-based EEG caps were carried out in 6 different countries with 115 volunteers, recording electrode-skin impedances, resting state EEG and evoked activity. The dry cap showed average channel reliability of 81% but higher average impedances than the gel-based cap. However, the dry EEG caps required 62% less preparation time. No statistical differences were observed between the gel-based and dry EEG signal characteristics in all signal metrics. We conclude that the performance of the dry multipin electrodes is highly reproducible, whereas the primary influences on channel reliability and signal quality are operator skill and experience.

Systemic Racism in EEG Research: Considerations and Potential Solutions

The current theoretical paper discusses the unintended systemic racism and racial biases that impact neuroscience, specifically in research utilizing electroencephalography (EEG). As a popular technique in affective science research, EEG requires adherence between the electrode and scalp to measure brain activity. To obtain high-quality data, various factors such as hair length, hair type, body movement, and/or extraneous noise from the environment are taken into consideration. As EEG researchers attempt to gather good-quality data, the recruitment and retention of Black American participants is challenging due to hairstyles commonly worn by Black American participants (e.g., cornrows, braids) and hair type. Taken together, the systemic lack of data from Black American participants renders research findings less generalizable and causes disparities in theoretical knowledge applicable to this population. To address this disparity, innovative solutions invented by bioengineers are discussed.

Audiovisual incongruence differentially impacts left and right hemisphere sensorimotor oscillations: Potential applications to production

Speech production gives rise to distinct auditory and somatosensory feedback signals which are dynamically integrated to enable online monitoring and error correction, though it remains unclear how the sensorimotor system supports the integration of these multimodal signals. Capitalizing on the parity of sensorimotor processes supporting perception and production, the current study employed the McGurk paradigm to induce multimodal sensory congruence/incongruence. EEG data from a cohort of 39 typical speakers were decomposed with independent component analysis to identify bilateral mu rhythms; indices of sensorimotor activity. Subsequent time-frequency analyses revealed bilateral patterns of event related desynchronization (ERD) across alpha and beta frequency ranges over the time course of perceptual events. Right mu activity was characterized by reduced ERD during all cases of audiovisual incongruence, while left mu activity was attenuated and protracted in McGurk trials eliciting sensory fusion. Results were interpreted to suggest distinct hemispheric contributions, with right hemisphere mu activity supporting a coarse incongruence detection process and left hemisphere mu activity reflecting a more granular level of analysis including phonological identification and incongruence resolution. Findings are also considered in regard to incongruence detection and resolution processes during production.

Sensorimotor contributions to working memory differ between the discrimination of Same and Different syllable pairs

Sensorimotor activity during speech perception is both pervasive and highly variable, changing as a function of the cognitive demands imposed by the task. The purpose of the current study was to evaluate whether the discrimination of Same (matched) and Different (unmatched) syllable pairs elicit different patterns of sensorimotor activity as stimuli are processed in working memory. Raw EEG data recorded from 42 participants were decomposed with independent component analysis to identify bilateral sensorimotor mu rhythms from 36 subjects. Time frequency decomposition of mu rhythms revealed concurrent event related desynchronization (ERD) in alpha and beta frequency bands across the peri- and post-stimulus time periods, which were interpreted as evidence of sensorimotor contributions to working memory encoding and maintenance. Left hemisphere alpha/beta ERD was stronger in Different trials than Same trials during the post-stimulus period, while right hemisphere alpha/beta ERD was stronger in Same trials than Different trials. A between-hemispheres contrast revealed no differences during Same trials, while post-stimulus alpha/beta ERD was stronger in the left hemisphere than the right during Different trials. Results were interpreted to suggest that predictive coding mechanisms lead to repetition suppression effects in Same trials. Mismatches arising from predictive coding mechanisms in Different trials shift subsequent working memory processing to the speech-dominant left hemisphere. Findings clarify how sensorimotor activity differentially supports working memory encoding and maintenance stages during speech discrimination tasks and have potential to inform sensorimotor models of speech perception and working memory.

Demystifying signal processing techniques to extract resting-state EEG features for psychologists

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non‐invasively. Some important mental functions could be encoded by resting‐state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting‐state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting‐state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting‐state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft‐used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.

Prediction of Clinical Outcomes With EEG Microstate in Patients With Major Depressive Disorder

Background: The difficulty in timely evaluating patient response to antidepressants has brought great challenge to the treatment of major depressive disorder (MDD). Some studies found that the electroencephalogram (EEG) microstates might be a reliable marker to evaluate patient response to treatment. The present study aims to evaluate the relationship between EEG microstate parameters and MDD symptoms before and after treatment to identify predictive biological markers for patient response. Methods: Thirty drug-naïve MDD patients (20 females and 10 males) were enrolled in this study. All the patients received effective dosages of selective serotonin reuptake inhibitors, and EEG recordings were collected at baseline and 2 weeks of treatment. Brain activities during the eyes-closed state were recorded using 64-channel electroencephalography, and the patients' microstates were clustered into four maps according to their topography (labeled A, B, C, and D). The differences of EEG microstates before and after treatment were compared using paired t-test. Spearman correlation coefficients were calculated to identify the relationships between the improvement of depression and anxiety symptoms and microstate parameters. Results: The mean duration (69.67 ± 10.33 vs. 64.00 ± 7.70, p < 0.001) and occurrence (4.06 ± 0.69, vs. 3.69 ± 0.70, p = 0.002) of microstate B decreased significantly after treatment. The proportion of microstate B also decreased (27.53 ± 5.81, vs. 23.23 ± 4.61, p < 0.001), while the occurrence of microstate A increased after treatment. A significant negative correlation was found between the change of score of Hamilton Rating Scale for Anxiety and the increase of the occurrence of microstate A (r = -0.431, p < 0.05) after 2 weeks of treatment. The reduction of the duration of microstate B was found to be predictive of patient response to antidepressants after 3 months. Conclusion: This study explored the relationship between changes of EEG microstates and patient response to antidepressants. Depression symptoms might be associated with the duration of microstate B and anxiety symptoms related to the occurrence of microstate A. Therefore, the duration of microstate B and the occurrence of microstate A are potential biological markers for MDD patients' early response and further clinical outcomes.

Neurophysiological correlates of cognitive control and approach motivation abnormalities in adolescent bipolar disorders

Hypersensitivity to reward-relevant stimuli is theorized to be a core etiological factor in bipolar disorders (BDs). However, little is known about the role of cognitive control dysregulation within reward contexts in BDs, particularly during adolescence. Using electroencephalography (EEG), we explored alterations in cognitive control processes and approach motivation in 99 adolescents with (n=53) and without (n=46) BD during reward striving (target anticipation) and reward attainment (feedback) phases of a monetary incentive delay (MID) task. Time-frequency analysis yielded frontal theta and frontal alpha asymmetry as indices of cognitive control and approach motivation, respectively. Multilevel mixed models examined group differences, as well as age, sex, and other effects, on frontal theta and frontal alpha asymmetry during both phases of the task and on performance accuracy and reaction times. Healthy adolescent girls exhibited lower frontal theta than both adolescent girls with BD and adolescent boys with and without BD during reward anticipation and feedback. Across groups, adolescent boys displayed greater relative left frontal alpha activity than adolescent girls during reward anticipation and feedback. Behaviorally, adolescents with BD exhibited faster responses on both positively and negatively motivated trials versus neutral trials, whereas healthy adolescents had faster responses only on positively motivated trials; adolescents with BD were less accurate in responding to neutral trials compared to healthy controls. These findings shed light on normative and BD-specific involvement of approach motivation and cognitive control during different stages of reward processing in adolescence and, further, provide evidence of adolescent sex differences in these processes.

Contact Pressure and Flexibility of Multipin Dry EEG Electrodes

In state-of-the-art electroencephalography (EEG) Silver/Silver-Chloride electrodes are applied together with electrolyte gels or pastes. Their application requires extensive preparation, trained medical staff and limits measurement time and mobility. We recently proposed a novel multichannel cap system for dry EEG electrodes for mobile and out-of-the-lab EEG acquisition. During the tests with these novel polymer-based multipin dry electrodes, we observed that the quality of the recording depends on the applied normal force and resulting contact pressure. Consequently, in this paper we systematically investigate the influence of electrode-skin contact pressure and electrode substrate flexibility on interfacial impedance and perceived wearing comfort in a study on 12 volunteers. The normal force applied to the electrode was varied between the minimum required force to achieve impedances and a maximum of 4 N, using a new force measurement applicator. We found that for a polymer shore hardness A98, with increasing normal force, the impedance decreases from and to and at frontal hairless and temporal hairy positions, respectively. Similar results were obtained for shore A90, A80, and A70. The best compromise of low and stable impedances as well as a good wearing comfort was determined for applied normal forces between 2 and 3 N using electrodes with shore A98 or A90. Our results provide the basis for improved EEG cap designs with optimal wearing comfort and recording quality for dry multipin electrodes, which will enable new fields of application for EEG.

Inherent physiological artifacts in EEG during tDCS

Online imaging and neuromodulation is invalid if stimulation distorts measurements beyond the point of accurate measurement. In theory, combining transcranial Direct Current Stimulation (tDCS) with electroencephalography (EEG) is compelling, as both use non-invasive electrodes and image-guided dose can be informed by the reciprocity principle. To distinguish real changes in EEG from stimulation artifacts, prior studies applied conventional signal processing techniques (e.g. high-pass filtering, ICA). Here, we address the assumptions underlying the suitability of these approaches. We distinguish physiological artifacts - defined as artifacts resulting from interactions between the stimulation induced voltage and the body and so inherent regardless of tDCS or EEG hardware performance - from methodology-related artifacts - arising from non-ideal experimental conditions or non-ideal stimulation and recording equipment performance. Critically, we identify inherent physiological artifacts which are present in all online EEG-tDCS: 1) cardiac distortion and 2) ocular motor distortion. In conjunction, non-inherent physiological artifacts which can be minimized in most experimental conditions include: 1) motion and 2) myogenic distortion. Artifact dynamics were analyzed for varying stimulation parameters (montage, polarity, current) and stimulation hardware. Together with concurrent physiological monitoring (ECG, respiration, ocular, EMG, head motion), and current flow modeling, each physiological artifact was explained by biological source-specific body impedance changes, leading to incremental changes in scalp DC voltage that are significantly larger than real neural signals. Because these artifacts modulate the DC voltage and scale with applied current, they are dose specific such that their contamination cannot be accounted for by conventional experimental controls (e.g. differing stimulation montage or current as a control). Moreover, because the EEG artifacts introduced by physiologic processes during tDCS are high dimensional (as indicated by Generalized Singular Value Decomposition- GSVD), non-stationary, and overlap highly with neurogenic frequencies, these artifacts cannot be easily removed with conventional signal processing techniques. Spatial filtering techniques (GSVD) suggest that the removal of physiological artifacts would significantly degrade signal integrity. Physiological artifacts, as defined here, would emerge only during tDCS, thus processing techniques typically applied to EEG in the absence of tDCS would not be suitable for artifact removal during tDCS. All concurrent EEG-tDCS must account for physiological artifacts that are a) present regardless of equipment used, and b) broadband and confound a broad range of experiments (e.g. oscillatory activity and event related potentials). Removal of these artifacts requires the recognition of their non-stationary, physiology-specific dynamics, and individualized nature. We present a broad taxonomy of artifacts (non/stimulation related), and suggest possible approaches and challenges to denoising online EEG-tDCS stimulation artifacts.

Novel Multipin Electrode Cap System for Dry Electroencephalography

Current usage of electroencephalography (EEG) is limited to laboratory environments. Self-application of a multichannel wet EEG caps is practically impossible, since the application of state-of-the-art wet EEG sensors requires trained laboratory staff. We propose a novel EEG cap system with multipin dry electrodes overcoming this problem. We describe the design of a novel 24-pin dry electrode made from polyurethane and coated with Ag/AgCl. A textile cap system holds 97 of these dry electrodes. An EEG study with 20 volunteers compares the 97-channel dry EEG cap with a conventional 128-channel wet EEG cap for resting state EEG, alpha activity, eye blink artifacts and checkerboard pattern reversal visual evoked potentials. All volunteers report a good cap fit and good wearing comfort. Average impedances are below 150 kΩ for 92 out of 97 dry electrodes, enabling recording with standard EEG amplifiers. No significant differences are observed between wet and dry power spectral densities for all EEG bands. No significant differences are observed between the wet and dry global field power time courses of visual evoked potentials. The 2D interpolated topographic maps show significant differences of 3.52 and 0.44% of the map areas for the N75 and N145 VEP components, respectively. For the P100 component, no significant differences are observed. Dry multipin electrodes integrated in a textile EEG cap overcome the principle limitations of wet electrodes, allow rapid application of EEG multichannel caps by non-trained persons, and thus enable new fields of application for multichannel EEG acquisition.

Alterations in the functional connectivity of frontal lobe networks preceding emergence delirium in children

BACKGROUND

This study aimed to characterize the electroencephalogram in children who emerged with emergence delirium (ED) compared with children without ED using methods that involved the assessment of cortical functional connectivity.

METHODS

Children aged 5 to 15 yr had multichannel electroencephalographic recordings during induction and emergence from anesthesia during minor surgical procedures. Of these, five children displayed ED after sevoflurane anesthesia. Measures of cortical functional connectivity previously used to evaluate anesthetic action in adults were compared between ED and age-, sex-, and anesthetic-matched non-ED children during emergence from anesthesia.

RESULTS

At the termination of sevoflurane anesthesia, the electroencephalogram in both ED and control patients showed delta frequency slowing and frontally dominant alpha activity, followed by a prolonged state with low-voltage, fast frequency activity (referred to as an indeterminate state). In children with ED, arousal with delirious behavior and a variety of electroencephalogram patterns occurred during the indeterminate state, before the appearance of normal wake or sleep patterns. The electroencephalogram in children without ED progressed from the indeterminate state to classifiable sleep or drowsy states, before peaceful awakening. Statistically significant differences in frontal lobe functional connectivity were identified between children with ED and non-ED.

CONCLUSIONS

ED is associated with arousal from an indeterminate state before the onset of sleep-like electroencephalogram patterns. Increased frontal lobe cortical functional connectivity observed in ED, immediately after the termination of sevoflurane anesthesia, will have important implications for the development of methods to predict ED, the design of preventative strategies, and efforts to better understand its pathophysiology.

Identifying electrode bridging from electrical distance distributions: a survey of publicly-available EEG data using a new method

OBJECTIVE

EEG topographies may be distorted by electrode bridges, typically caused by electrolyte spreading between adjacent electrodes. We therefore sought to determine the prevalence of electrode bridging and its potential impact on the EEG literature.

METHODS

Five publicly-available EEG datasets were evaluated for evidence of bridging using a new screening method that employs the temporal variance of pairwise difference waveforms (electrical distance). Distinctive characteristics of electrical distance frequency distributions were used to develop an algorithm to identify electrode bridges in datasets with different montages (22-64 channels) and noise properties.

RESULTS

The extent of bridging varied substantially across datasets: 54% of EEG recording sessions contained an electrode bridge, and the mean percentage of bridged electrodes in a montage was as high as 18% in one of the datasets. Furthermore, over 40% of the recording channels were bridged in 9 of 203 sessions. These findings were independently validated by visual inspection.

CONCLUSIONS

The new algorithm conveniently, efficiently, and reliably identified electrode bridges across different datasets and recording conditions. Electrode bridging may constitute a substantial problem for some datasets.

SIGNIFICANCE

Given the extent of the electrode bridging across datasets, this problem may be more widespread than commonly thought. However, when used as an automatic screening routine, the new algorithm will prevent pitfalls stemming from unrecognized electrode bridges.

Generator localization by current source density (CSD): implications of volume conduction and field closure at intracranial and scalp resolutions

The topographic ambiguity and reference-dependency that has plagued EEG/ERP research throughout its history are largely attributable to volume conduction, which may be concisely described by a vector form of Ohm's Law. This biophysical relationship is common to popular algorithms that infer neuronal generators via inverse solutions. It may be further simplified as Poisson's source equation, which identifies underlying current generators from estimates of the second spatial derivative of the field potential (Laplacian transformation). Intracranial current source density (CSD) studies have dissected the "cortical dipole" into intracortical sources and sinks, corresponding to physiologically-meaningful patterns of neuronal activity at a sublaminar resolution, much of which is locally cancelled (i.e., closed field). By virtue of the macroscopic scale of the scalp-recorded EEG, a surface Laplacian reflects the radial projections of these underlying currents, representing a unique, unambiguous measure of neuronal activity at scalp. Although the surface Laplacian requires minimal assumptions compared to complex, model-sensitive inverses, the resulting waveform topographies faithfully summarize and simplify essential constraints that must be placed on putative generators of a scalp potential topography, even if they arise from deep or partially-closed fields. CSD methods thereby provide a global empirical and biophysical context for generator localization, spanning scales from intracortical to scalp recordings.

High density electroencephalography in sleep research: potential, problems, future perspective

High density EEG (hdEEG) during sleep combines the superior temporal resolution of EEG recordings with high spatial resolution. Thus, this method allows a topographical analysis of sleep EEG activity and thereby fosters the shift from a global view of sleep to a local one. HdEEG allowed to investigate sleep rhythms in terms of their characteristic behavior (e.g., the traveling of slow waves) and in terms of their relationship to cortical functioning (e.g., consciousness and cognitive abilities). Moreover, recent studies successfully demonstrated that hdEEG can be used to study brain functioning in neurological and neuro-developmental disorders, and to evaluate therapeutic approaches. This review highlights the potential, the problems, and future perspective of hdEEG in sleep research.

"What" and "where" in auditory sensory processing: a high-density electrical mapping study of distinct neural processes underlying sound object recognition and sound localization

Functionally distinct dorsal and ventral auditory pathways for sound localization (WHERE) and sound object recognition (WHAT) have been described in non-human primates. A handful of studies have explored differential processing within these streams in humans, with highly inconsistent findings. Stimuli employed have included simple tones, noise bursts, and speech sounds, with simulated left–right spatial manipulations, and in some cases participants were not required to actively discriminate the stimuli. Our contention is that these paradigms were not well suited to dissociating processing within the two streams. Our aim here was to determine how early in processing we could find evidence for dissociable pathways using better titrated WHAT and WHERE task conditions. The use of more compelling tasks should allow us to amplify differential processing within the dorsal and ventral pathways. We employed high-density electrical mapping using a relatively large and environmentally realistic stimulus set (seven animal calls) delivered from seven free-field spatial locations; with stimulus configuration identical across the “WHERE” and “WHAT” tasks. Topographic analysis revealed distinct dorsal and ventral auditory processing networks during the WHERE and WHAT tasks with the earliest point of divergence seen during the N1 component of the auditory evoked response, beginning at approximately 100 ms. While this difference occurred during the N1 timeframe, it was not a simple modulation of N1 amplitude as it displayed a wholly different topographic distribution to that of the N1. Global dissimilarity measures using topographic modulation analysis confirmed that this difference between tasks was driven by a shift in the underlying generator configuration. Minimum-norm source reconstruction revealed distinct activations that corresponded well with activity within putative dorsal and ventral auditory structures.

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 (1-5Hz), alpha-low (6-9Hz), alpha-high (10-11Hz), beta (12-19Hz), and gamma (>21Hz). 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.

Validation of ICA-based myogenic artifact correction for scalp and source-localized EEG

Muscle electrical activity, or "electromyogenic" (EMG) artifact, poses a serious threat to the validity of electroencephalography (EEG) investigations in the frequency domain. EMG is sensitive to a variety of psychological processes and can mask genuine effects or masquerade as legitimate neurogenic effects across the scalp in frequencies at least as low as the alpha band (8-13 Hz). Although several techniques for correcting myogenic activity have been described, most are subjected to only limited validation attempts. Attempts to gauge the impact of EMG correction on intracerebral source models (source "localization" analyses) are rarer still. Accordingly, we assessed the sensitivity and specificity of one prominent correction tool, independent component analysis (ICA), on the scalp and in the source-space using high-resolution EEG. Data were collected from 17 participants while neurogenic and myogenic activity was independently varied. Several protocols for classifying and discarding components classified as myogenic and non-myogenic artifact (e.g., ocular) were systematically assessed, leading to the exclusion of one-third to as much as three-quarters of the variance in the EEG. Some, but not all, of these protocols showed adequate performance on the scalp. Indeed, performance was superior to previously validated regression-based techniques. Nevertheless, ICA-based EMG correction exhibited low validity in the intracerebral source-space, likely owing to incomplete separation of neurogenic from myogenic sources. Taken with prior work, this indicates that EMG artifact can substantially distort estimates of intracerebral spectral activity. Neither regression- nor ICA-based EMG correction techniques provide complete safeguards against such distortions. In light of these results, several practical suggestions and recommendations are made for intelligently using ICA to minimize EMG and other common artifacts.

Hemispheric differences in auditory oddball responses during monaural versus binaural stimulation

Hemispheric lateralization of early event-related potentials (ERPs; e.g. N1) is largely based on anatomy of the afferent pathway; lateralization of later auditory ERPs (P2/N2, P250, P3b) is less clear. Using 257-channel EEG, the present study examined hemispheric laterality of auditory ERPs by comparing binaural and monaural versions of an auditory oddball task. N1 showed a contralateral bias over auditory cortex in both hemispheres as a function of ear of stimulation, although right hemisphere sources were activated regardless of which ear received input. Beginning around N1 and continuing through the time of P3b, right hemisphere temporal-parietal and frontal areas were more activated than their left hemisphere counterparts for stimulus evaluation/comparison and target detection. P250 and P3b component amplitudes, topographies, and source estimations were significantly influenced by ear of stimulation, with right hemisphere activity being stronger. This was particularly true for anterior temporal and inferior frontal sources which were more strongly associated with the later, more cognitive components (P250, P3b). Results are consistent with theories of a right hemisphere network that is prominently involved in sustained attention, stimulus evaluation, target detection, and working memory/context updating.

Validation of regression-based myogenic correction techniques for scalp and source-localized EEG

EEG and EEG source-estimation are susceptible to electromyographic artifacts (EMG) generated by the cranial muscles. EMG can mask genuine effects or masquerade as a legitimate effect-even in low frequencies, such as alpha (8-13 Hz). Although regression-based correction has been used previously, only cursory attempts at validation exist, and the utility for source-localized data is unknown. To address this, EEG was recorded from 17 participants while neurogenic and myogenic activity were factorially varied. We assessed the sensitivity and specificity of four regression-based techniques: between-subjects, between-subjects using difference-scores, within-subjects condition-wise, and within-subject epoch-wise on the scalp and in data modeled using the LORETA algorithm. Although within-subject epoch-wise showed superior performance on the scalp, no technique succeeded in the source-space. Aside from validating the novel epoch-wise methods on the scalp, we highlight methods requiring further development.

EEG functional connectivity in term age extremely low birth weight infants

OBJECTIVE

The hypothesis is tested that electrocortical functional connectivity (quantified by coherence) of extremely low birth weight (ELBW) infants, measured at term post-menstrual age, has regional differences from that of full term infants.

METHODS

128 lead EEG data were collected during sleep from 8 ELBW infants with normal head ultrasound exams and 8 typically developing full term infants. Regional spectral power and coherence were calculated.

RESULTS

No significant regional differences in EEG power were found between infant groups. However, compared to term infants, ELBW infants had significantly reduced interhemispheric coherence (in frontal polar and parietal regions) and intrahemispheric coherence (between frontal polar and parieto-occipital regions) in the 1-12Hz band but increased interhemispheric coherence between occipital regions in the 24-50Hz band.

CONCLUSIONS

ELBW infants at term post-menstrual age manifest regional differences in EEG functional connectivity as compared to term infants.

SIGNIFICANCE

Distinctive spatial patterns of electrocortical synchrony are found in ELBW infants. These regional patterns may presage regional alterations in the structure of the cortex.

Electrocortical functional connectivity in infancy: response to body tilt

To test the hypothesis that infant cortical regions activated by a head-up tilt also exhibit increased functional electrocortical connectivity, prone sleeping newborn and 2- to 4-month-old infants were tilted head-up to 30 degrees. Electroencephalogram (EEG) data were collected with 128 electrodes and coherence calculated to quantify electrocortical synchrony. Local coherence, defined as the average of coherence measurements between the EEG at each electrode site and neighboring sites (approximately 1 cm electrode spacing), was found in activated cortical regions that had previously shown increased high-frequency power with tilt. Long-distance coherence was computed between the regions. Newborn infants had significant increases in local coherence in the activated left frontal, right frontal-temporal, and occipital cortical regions; long-distance coherence increased between the right frontal-temporal and occipital regions. In contrast, infants at 2 to 4 months old, the age of maximum risk for sudden infant death syndrome, had no significant changes in coherence. Newborn and 2- to 4-month-old infants thus have different electrocortical responses to a classic cardiovascular challenge.

Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: II. Adequacy of low-density estimates

OBJECTIVE

To evaluate the comparability of high- and low-density surface Laplacian estimates for determining ERP generator patterns of group data derived from a typical ERP sample size and paradigm.

METHODS

High-density ERP data (129 sites) recorded from 17 adults during tonal and phonetic oddball tasks were converted to a 10-20-system EEG montage (31 sites) using spherical spline interpolations. Current source density (CSD) waveforms were computed from the high- and low-density, but otherwise identical, ERPs, and correlated at corresponding locations. CSD data were submitted to separate covariance-based, unrestricted temporal PCAs (Varimax of covariance loadings) to identify and effectively summarize temporally and spatially overlapping CSD components. Solutions were compared by correlating factor loadings and scores, and by plotting ANOVA F statistics derived from corresponding high- and low-resolution factor scores using representative sites.

RESULTS

High- and low-density CSD waveforms, PCA solutions, and F statistics were remarkably similar, yielding correlations of .9 < or = r < or = .999 between waveforms, loadings, and scores for almost all comparisons at low-density locations except for low-signal CSD waveforms at occipital sites. Each of the first 10 high-density factors corresponded precisely to one factor of the first 10 low-density factors, with each 10-factor set accounting for the meaningful CSD variance (> 91.6%).

CONCLUSIONS

Low-density surface Laplacian estimates were shown to be accurate approximations of high-density CSDs at these locations, which adequately and quite sufficiently summarized group data. Moreover, reasonable approximations of many high-density scalp locations were obtained for group data from interpolations of low-density data. If group findings are the primary objective, as typical for cognitive ERP research, low-resolution CSD topographies may be as efficient, given the effective spatial smoothing when averaging across subjects and/or conditions.

SIGNIFICANCE

Conservative recommendations for restricting surface Laplacians to high-density recordings may not be appropriate for all ERP research applications, and should be re-evaluated considering objective, costs and benefits.