Accounting for microsaccadic artifacts in the EEG using independent component analysis and beamforming

Multi-Algorithm Artifact Correction (MAAC) procedure part one: Algorithm and example

The Multi-Algorithm Artifact Correction (MAAC) procedure is presented for electroencephalographic (EEG) data, as made freely available in the open-source EP Toolkit (Dien, 2010). First the major EEG artifact correction methods (regression, spatial filters, principal components analysis, and independent components analysis) are reviewed. Contrary to the dominant approach of picking one method that is thought to be most effective, this review concludes that none are globally superior, but rather each has strengths and weaknesses. Then each of the major artifact types are reviewed (Blink, Corneo-Retinal Dipole, Saccadic Spike Potential, and Movement). For each one, it is proposed that one of the major correction methods is best matched to address it, resulting in the MAAC procedure. The MAAC itself is then presented, as implemented in the EP Toolkit, in order to provide a sense of the user experience. The primary goal of this present paper is to make the conceptual argument for the MAAC approach.

Naturalistic viewing conditions can increase task engagement and aesthetic preference but have only minimal impact on EEG quality

Free gaze and moving images are typically avoided in EEG experiments due to the expected generation of artifacts and noise. Yet for a growing number of research questions, loosening these rigorous restrictions would be beneficial. Among these is research on visual aesthetic experiences, which often involve open-ended exploration of highly variable stimuli. Here we systematically compare the effect of conservative vs. more liberal experimental settings on various measures of behavior, brain activity and physiology in an aesthetic rating task. Our primary aim was to assess EEG signal quality. 43 participants either maintained fixation or were allowed to gaze freely, and viewed either static images or dynamic (video) stimuli consisting of dance performances or nature scenes. A passive auditory background task (auditory steady-state response; ASSR) was added as a proxy measure for overall EEG recording quality. We recorded EEG, ECG and eyetracking data, and participants rated their aesthetic preference and state of boredom on each trial. Whereas both behavioral ratings and gaze behavior were affected by task and stimulus manipulations, EEG SNR was barely affected and generally robust across all conditions, despite only minimal preprocessing and no trial rejection. In particular, we show that using video stimuli does not necessarily result in lower EEG quality and can, on the contrary, significantly reduce eye movements while increasing both the participants' aesthetic response and general task engagement. We see these as encouraging results indicating that - at least in the lab - more liberal experimental conditions can be adopted without significant loss of signal quality.

Theta oscillations: A rhythm difference comparison between major depressive disorder and anxiety disorder

BACKGROUND

Due to substantial comorbidities of major depressive disorder (MDD) and anxiety disorder (AN), these two disorders must be distinguished. Accurate identification and diagnosis facilitate effective and prompt treatment. EEG biomarkers are a potential research hotspot for neuropsychiatric diseases. The purpose of this study was to investigate the differences in EEG power spectrum at theta oscillations between patients with MDD and patients with AN.

METHODS

Spectral analysis was used to study 66 patients with MDD and 43 patients with AN. Participants wore 16-lead EEG caps to measure resting EEG signals. The EEG power spectrum was measured using the fast Fourier transform. Independent samples t-test was used to analyze the EEG power values of the two groups, and p < 0.05 was statistically significant.

RESULTS

EEG power spectrum of the MDD group significantly differed from the AN group in the theta oscillation on 4-7 Hz at eight electrode points at F3, O2, T3, P3, P4, FP1, FP2, and F8.

CONCLUSION

Participants with anxiety demonstrated reduced power in the prefrontal cortex, left temporal lobe, and right occipital regions. Confirmed by further studies, theta oscillations could be another biomarker that distinguishes MDD from AN.

SUPFUNSIM: Spatial Filtering Toolbox for EEG

Brain activity pattern recognition from EEG or MEG signal analysis is one of the most important method in cognitive neuroscience. The supFunSim library is a new Matlab toolbox which generates accurate EEG forward model and implements a collection of spatial filters for EEG source reconstruction, including the linearly constrained minimum-variance (LCMV), eigenspace LCMV, nulling (NL), and minimum-variance pseudo-unbiased reduced-rank (MV-PURE) filters in various versions. It also enables source-level directed connectivity analysis using partial directed coherence (PDC) measure. The supFunSim library is based on the well-known FieldTrip toolbox for EEG and MEG analysis and is written using object-oriented programming paradigm. The resulting modularity of the toolbox enables its simple extensibility. This paper gives a complete overview of the toolbox from both developer and end-user perspectives, including description of the installation process and use cases.

Regression-based analysis of combined EEG and eye-tracking data: Theory and applications

Fixation-related potentials (FRPs), neural responses aligned to the end of saccades, are a promising tool for studying the dynamics of attention and cognition under natural viewing conditions. In the past, four methodological problems have complicated the analysis of such combined eye-tracking/electroencephalogram experiments: (1) the synchronization of data streams, (2) the removal of ocular artifacts, (3) the condition-specific temporal overlap between the brain responses evoked by consecutive fixations, and (4) the fact that numerous low-level stimulus and saccade properties also influence the postsaccadic neural responses. Although effective solutions exist for the first two problems, the latter two are only beginning to be addressed. In the current paper, we present and review a unified regression-based framework for FRP analysis that allows us to deconvolve overlapping potentials while also controlling for both linear and nonlinear confounds on the FRP waveform. An open software implementation is provided for all procedures. We then demonstrate the advantages of this proposed (non)linear deconvolution modeling approach for data from three commonly studied paradigms: face perception, scene viewing, and reading. First, for a traditional event-related potential (ERP) face recognition experiment, we show how this technique can separate stimulus ERPs from overlapping muscle and brain potentials produced by small (micro)saccades on the face. Second, in natural scene viewing, we model and isolate multiple nonlinear effects of saccade parameters on the FRP. Finally, for a natural sentence reading experiment using the boundary paradigm, we show how it is possible to study the neural correlates of parafoveal preview after removing spurious overlap effects caused by the associated difference in average fixation time. Our results suggest a principal way of measuring reliable eye movement-related brain activity during natural vision.

Unaltered EEG spectral power and functional connectivity in REM microstates in frequent nightmare recallers: are nightmares really a REM parasomnia?

BACKGROUND

Frequent nightmares show signs of hyperarousal in NREM sleep. Nevertheless, idiopathic nightmare disorder is considered a REM parasomnia, but the pathophysiology of REM sleep in relation to frequent nightmares is controversial. Cortical oscillatory activity in REM sleep is largely modulated by phasic and tonic REM periods and seems to be linked to different functions and dysfunctions of REM sleep. Here, we examined cortical activity and functional synchronization in frequent nightmare recallers and healthy controls, during phasic and tonic REM.

METHODS

Frequent nightmare recallers (N = 22) and healthy controls (N = 22) matched for high dream recall spent two nights in the laboratory. Phasic and tonic REM periods from the second nights' recordings were selected to examine differences in EEG spectral power and weighted phase lag index (WPLI) across groups and REM states.

RESULTS

Phasic REM showed increased power and synchronization in delta and gamma frequency bands, whereas tonic REM featured increased power and synchronization in the alpha and beta bands. In the theta band, power was higher during tonic, and synchronization was higher during phasic REM sleep. No differences across nightmare and control participants or patterns representing interactions between the groups and REM microstates emerged.

CONCLUSIONS

Our findings do not support the idea that abnormal REM sleep power and synchronization play a role in the pathophysiology of frequent nightmares. Altered REM sleep in nightmare disorder could have been confounded with comorbid pathologies and increased dream recall, or might be linked to more specific state factors (nightmare episodes).

Dynamic activity patterns in the anterior temporal lobe represents object semantics

The anterior temporal lobe (ATL) is considered a crucial area for the representation of transmodal concepts. Recent evidence suggests that specific regions within the ATL support the representation of individual object concepts, as shown by studies combining multivariate analysis methods and explicit measures of semantic knowledge. This research looks to further our understanding by probing conceptual representations at a spatially and temporally resolved neural scale. Representational similarity analysis was applied to human intracranial recordings from anatomically defined lateral to medial ATL sub-regions. Neural similarity patterns were tested against semantic similarity measures, where semantic similarity was defined by a hybrid corpus-based and feature-based approach. Analyses show that the perirhinal cortex, in the medial ATL, significantly related to semantic effects around 200 to 400 ms, and were greater than more lateral ATL regions. Further, semantic effects were present in low frequency (theta and alpha) oscillatory phase signals. These results provide converging support that more medial regions of the ATL support the representation of basic-level visual object concepts within the first 400 ms, and provide a bridge between prior fMRI and MEG work by offering detailed evidence for the presence of conceptual representations within the ATL.

Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments

Combining EEG with eye-tracking is a promising approach to study neural correlates of natural vision, but the resulting recordings are also heavily contaminated by activity of the eye balls, eye lids, and extraocular muscles. While Independent Component Analysis (ICA) is commonly used to suppress these ocular artifacts, its performance under free viewing conditions has not been systematically evaluated and many published reports contain residual artifacts. Here I evaluated and optimized ICA-based correction for two tasks with unconstrained eye movements: visual search in images and sentence reading. In a first step, four parameters of the ICA pipeline were varied orthogonally: the (1) high-pass and (2) low-pass filter applied to the training data, (3) the proportion of training data containing myogenic saccadic spike potentials (SP), and (4) the threshold for eye tracker-based component rejection. In a second step, the eye-tracker was used to objectively quantify the correction quality of each ICA solution, both in terms of undercorrection (residual artifacts) and overcorrection (removal of neurogenic activity). As a benchmark, results were compared to those obtained with an alternative spatial filter, Multiple Source Eye Correction (MSEC). With commonly used settings, Infomax ICA not only left artifacts in the data, but also distorted neurogenic activity during eye movement-free intervals. However, correction results could be strongly improved by training the ICA on optimally filtered data in which SPs were massively overweighted. With optimized procedures, ICA removed virtually all artifacts, including the SP and its associated spectral broadband artifact from both viewing paradigms, with little distortion of neural activity. It also outperformed MSEC in terms of SP correction. Matlab code is provided.

Alterations in oscillatory cortical activity indicate changes in mnemonic processing during continuous item recognition

The classification of repeating stimuli as either old or new is a general mechanism of everyday perception. However, the cortical mechanisms underlying this process are not fully understood. In general, mnemonic processes are thought to rely on changes in oscillatory brain activity across several frequencies as well as their interaction. Lower frequencies, mainly theta-band (3-7 Hz) and alpha-band (8-14 Hz) activity, are attributed to executive control and resource management, respectively; whereas recent studies revealed higher frequencies, e.g. gamma-band (> 25 Hz) activity, to reflect the activation of cortical object representations. Furthermore, low-frequency phase to high-frequency amplitude coupling (PAC) was recently found to coordinate the involved mnemonic networks. To further unravel the processes behind memorization of repeatedly presented stimuli, we applied a continuous item recognition task with up to five presentations per item (mean time between repetitions ~ 10 s) while recording high-density EEG. We examined spectral amplitude modulations as well as PAC. We observed theta amplitudes reaching a peak at second presentation, a reduction of alpha suppression after second presentation, decreased response time, as well as reduced theta-gamma PAC (3 to 7 to - 30 to 45 Hz) at frontal sites after third presentation. We conclude a shift from an explicit- to an implicit-like mnemonic processing, occurring around third presentation, with theta power to signify encoding of repetition-based episodic information and PAC as a neural correlate of the coordination of local neural networks.

The differentiated networks related to essential tremor onset and its amplitude modulation after alcohol intake

The dysregulation of endogenous rhythms within brain networks have been implicated in a broad range of motor and non-motor pathologies. Essential tremor (ET), classically the purview of a single aberrant pacemaker, has recently become associated with network-level dysfunction across multiple brain regions. Specifically, it has been suggested that motor cortex constitutes an important node in a tremor-generating network involving the cerebellum. Yet the mechanisms by which these regions relate to tremor remain a matter of considerable debate. We sought to discriminate the contributions of cerebral and cerebellar dysregulation by combining high-density electroencephalography with subject-specific structural MRI. For that, we contrasted ET with voluntary (mimicked) tremor before and after ingestion of alcohol to regulate the tremorgenic networks. Our results demonstrate distinct loci of cortical tremor coherence, most pronounced over the sensorimotor cortices in healthy controls, but more frontal motor areas in ET-patients consistent with a heightened involvement of the supplementary motor area. We further demonstrate that the reduction in tremor amplitude associated with alcohol intake is reflected in altered cerebellar - but not cerebral - coupling with movement. Taken together, these findings implicate tremor emergence as principally associated with increases in activity within frontal motor regions, whereas modulation of the amplitude of established tremor relates to changes in cerebellar activity. These findings progress a mechanistic understanding of ET and implicate network-level vulnerabilities in the rhythmic nature of communication throughout the brain.

A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords

Experimental evidence indicates that neurophysiological responses to well-known meaningful sensory items and symbols (such as familiar objects, faces, or words) differ from those to matched but novel and senseless materials (unknown objects, scrambled faces, and pseudowords). Spectral responses in the high beta- and gamma-band have been observed to be generally stronger to familiar stimuli than to unfamiliar ones. These differences have been hypothesized to be caused by the activation of distributed neuronal circuits or cell assemblies, which act as long-term memory traces for learned familiar items only. Here, we simulated word learning using a biologically constrained neurocomputational model of the left-hemispheric cortical areas known to be relevant for language and conceptual processing. The 12-area spiking neural-network architecture implemented replicates physiological and connectivity features of primary, secondary, and higher-association cortices in the frontal, temporal, and occipital lobes of the human brain. We simulated elementary aspects of word learning in it, focussing specifically on semantic grounding in action and perception. As a result of spike-driven Hebbian synaptic plasticity mechanisms, distributed, stimulus-specific cell-assembly (CA) circuits spontaneously emerged in the network. After training, presentation of one of the learned "word" forms to the model correlate of primary auditory cortex induced periodic bursts of activity within the corresponding CA, leading to oscillatory phenomena in the entire network and spontaneous across-area neural synchronization. Crucially, Morlet wavelet analysis of the network's responses recorded during presentation of learned meaningful "word" and novel, senseless "pseudoword" patterns revealed stronger induced spectral power in the gamma-band for the former than the latter, closely mirroring differences found in neurophysiological data. Furthermore, coherence analysis of the simulated responses uncovered dissociated category specific patterns of synchronous oscillations in distant cortical areas, including indirectly connected primary sensorimotor areas. Bridging the gap between cellular-level mechanisms, neuronal-population behavior, and cognitive function, the present model constitutes the first spiking, neurobiologically, and anatomically realistic model able to explain high-frequency oscillatory phenomena indexing language processing on the basis of dynamics and competitive interactions of distributed cell-assembly circuits which emerge in the brain as a result of Hebbian learning and sensorimotor experience.

Modulation of microsaccades by spatial frequency during object categorization

The organization of visual processing into a coarse-to-fine information processing based on the spatial frequency properties of the input forms an important facet of the object recognition process. During visual object categorization tasks, microsaccades occur frequently. One potential functional role of these eye movements is to resolve high spatial frequency information. To assess this hypothesis, we examined the rate, amplitude and speed of microsaccades in an object categorization task in which participants viewed object and non-object images and classified them as showing either natural objects, man-made objects or non-objects. Images were presented unfiltered (broadband; BB) or filtered to contain only low (LSF) or high spatial frequency (HSF) information. This allowed us to examine whether microsaccades were modulated independently by the presence of a high-level feature - the presence of an object - and by low-level stimulus characteristics - spatial frequency. We found a bimodal distribution of saccades based on their amplitude, with a split between smaller and larger microsaccades at 0.4° of visual angle. The rate of larger saccades (⩾0.4°) was higher for objects than non-objects, and higher for objects with high spatial frequency content (HSF and BB objects) than for LSF objects. No effects were observed for smaller microsaccades (<0.4°). This is consistent with a role for larger microsaccades in resolving HSF information for object identification, and previous evidence that more microsaccades are directed towards informative image regions.