Marissa Westerfield

Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects

OBJECTIVES Electrical potentials produced by blinks and eye movements present serious problems for electroencephalographic (EEG) and event-related potential (ERP) data interpretation and analysis, particularly for analysis of data from some clinical populations. Often, all epochs contaminated by large eye artifacts are rejected as unusable, though this may prove unacceptable when blinks and eye movements occur frequently. METHODS Frontal channels are often used as reference signals to regress out eye artifacts, but inevitably portions of relevant EEG signals also appearing in EOG channels are thereby eliminated or mixed into other scalp channels. A generally applicable adaptive method for removing artifacts from EEG records based on blind source separation by independent component analysis (ICA) (Neural Computation 7 (1995) 1129; Neural Computation 10(8) (1998) 2103; Neural Computation 11(2) (1999) 606) overcomes these limitations. RESULTS Results on EEG data collected from 28 normal controls and 22 clinical subjects performing a visual selective attention task show that ICA can be used to effectively detect, separate and remove ocular artifacts from even strongly contaminated EEG recordings. The results compare favorably to those obtained using rejection or regression methods. CONCLUSIONS The ICA method can preserve ERP contributions from all of the recorded trials and all the recorded data channels, even when none of the single trials are artifact-free.

Analysis and visualization of single-trial event-related potentials

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single‐trial multichannel EEG data from event‐related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra‐brain sources. Both the data and their decomposition are displayed using a new visualization tool, the “ERP image,” that can clearly characterize single‐trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye‐movement artifacts. Results show that ICA can separate artifactual, stimulus‐locked, response‐locked, and non‐event‐related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single‐trial EEG records, (2) identification and segregation of stimulus‐ and response‐locked EEG components, (3) examination of differences in single‐trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between‐subject component stability of ICA decomposition of single‐trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event‐related potentials, and event‐modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event‐related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Hum. Brain Mapping 14:166–185, 2001.

Imaging human EEG dynamics using independent component analysis.

This review discusses the theory and practical application of independent component analysis (ICA) to multi-channel EEG data. We use examples from an audiovisual attention-shifting task performed by young and old subjects to illustrate the power of ICA to resolve subtle differences between evoked responses in the two age groups. Preliminary analysis of these data using ICA suggests a loss of task specificity in independent component (IC) processes in frontal and somatomotor cortex during post-response periods in older as compared to younger subjects, trends not detected during examination of scalp-channel event-related potential (ERP) averages. We discuss possible approaches to component clustering across subjects and new ways to visualize mean and trial-by-trial variations in the data, including ERP-image plots of dynamics within and across trials as well as plots of event-related spectral perturbations in component power, phase locking, and coherence. We believe that widespread application of these and related analysis methods should bring EEG once again to the forefront of brain imaging, merging its high time and frequency resolution with enhanced cm-scale spatial resolution of its cortical sources.

Electroencephalographic brain dynamics following manually responded visual targets.

Scalp-recorded electroencephalographic (EEG) signals produced by partial synchronization of cortical field activity mix locally synchronous electrical activities of many cortical areas. Analysis of event-related EEG signals typically assumes that poststimulus potentials emerge out of a flat baseline. Signals associated with a particular type of cognitive event are then assessed by averaging data from each scalp channel across trials, producing averaged event-related potentials (ERPs). ERP averaging, however, filters out much of the information about cortical dynamics available in the unaveraged data trials. Here, we studied the dynamics of cortical electrical activity while subjects detected and manually responded to visual targets, viewing signals retained in ERP averages not as responses of an otherwise silent system but as resulting from event-related alterations in ongoing EEG processes. We applied infomax independent component analysis to parse the dynamics of the unaveraged 31-channel EEG signals into maximally independent processes, then clustered the resulting processes across subjects by similarities in their scalp maps and activity power spectra, identifying nine classes of EEG processes with distinct spatial distributions and event-related dynamics. Coupled two-cycle postmotor theta bursts followed button presses in frontal midline and somatomotor clusters, while the broad postmotor “P300” positivity summed distinct contributions from several classes of frontal, parietal, and occipital processes. The observed event-related changes in local field activities, within and between cortical areas, may serve to modulate the strength of spike-based communication between cortical areas to update attention, expectancy, memory, and motor preparation during and after target recognition and speeded responding.

Medial Prefrontal Theta Bursts Precede Rapid Motor Responses during Visual Selective Attention

After visual target stimuli presented infrequently at a covertly attended location, quicker speeded button presses immediately followed a larger positive (P3f) ramp in averaged electroencephalographic (EEG) recordings from the forehead. We show this peak in the mean response time locked to the button press to be principally composed of triphasic, primarily low-theta band (4.5 Hz) complexes preceding but only partially phase-locked to the button press, with larger complexes preceding quicker motor responses. For 10 of 15 subjects, independent component analysis of the unaveraged 31-channel data identified a temporally independent medial frontal EEG process contributing to these phenomena. Low-resolution tomographic modeling localized related components of two 253-channel data sets to medial frontal polar cortex (BA32/10). The far-frontal low-theta complexes and concomitant mean P3f positivity may index cortical activity induced by paralimbic processes involved in disinhibiting impulsive motor responses to rewarding or goal-fulfilling stimuli or events.

Modality-specificity of sensory aging in vision and audition: Evidence from event-related potentials

Major accounts of aging implicate changes in processing external stimulus information. Little is known about differential effects of auditory and visual sensory aging, and the mechanisms of sensory aging are still poorly understood. Using event-related potentials (ERPs) elicited by unattended stimuli in younger (M=25.5 yrs) and older (M=71.3 yrs) subjects, this study examined mechanisms of sensory aging under minimized attention conditions. Auditory and visual modalities were examined to address modality-specificity vs. generality of sensory aging. Between-modality differences were robust. The earlier-latency responses (P1, N1) were unaffected in the auditory modality but were diminished in the visual modality. The auditory N2 and early visual N2 were diminished. Two similarities between the modalities were age-related enhancements in the late P2 range and positive behavior-early N2 correlation, the latter suggesting that N2 may reflect long-latency inhibition of irrelevant stimuli. Since there is no evidence for salient differences in neuro-biological aging between the two sensory regions, the observed between-modality differences are best explained by the differential reliance of auditory and visual systems on attention. Visual sensory processing relies on facilitation by visuo-spatial attention, withdrawal of which appears to be more disadvantageous in older populations. In contrast, auditory processing is equipped with powerful inhibitory capacities. However, when the whole auditory modality is unattended, thalamo-cortical gating deficits may not manifest in the elderly. In contrast, ERP indices of longer-latency, stimulus-level inhibitory modulation appear to diminish with age.

The Functional Neuroanatomy of Spatial Attention in Autism Spectrum Disorder

This study investigated the functional neuroanatomical correlates of spatial attention impairments in autism spectrum disorders (ASD) using an event-related functional magnetic resonance imaging (FMRI) design. Eight ASD participants and 8 normal comparison (NC) participants were tested with a task that required stimulus discrimination following a spatial cue that preceded target presentation by 100 msec (short interstimulus interval [ISI]) or 800 msec (long ISI). The ASD group showed significant behavioral spatial attention impairment in the short ISI condition. The FMRI results showed a reduction in activity within frontal, parietal, and occipital regions in ASD relative to the NC group, most notably within the inferior parietal lobule. ASD behavioral performance improved in the long ISI condition but was still impaired relative to the NC group. ASD FMRI activity in the long ISI condition suggested that the rudimentary framework of normal attention networks were engaged in ASD including bilateral activation within the frontal, parietal, and occipital lobes. Notable activation increases were observed in the superior parietal lobule and extrastriate cortex. No reliable activation was observed in the posterior cerebellar vermis in ASD participants during either long or short ISI conditions. In addition, no frontal activation during short ISI and severely reduced frontal activation during long ISI was observed in the ASD group. Taken together, these findings suggest a dysfunctional cerebello-frontal spatial attention system in ASD. The pattern of findings suggests that ASD is associated with a profound deficit in automatic spatial attention abilities and abnormal voluntary spatial attention abilities. This article also describes a method for reducing the contribution of physical eye movements to the blood-oxygenation level dependent activity in studies of ASD.

Detection and classification of subject-generated artifacts in EEG signals using autoregressive models.

We examine the problem of accurate detection and classification of artifacts in continuous EEG recordings. Manual identification of artifacts, by means of an expert or panel of experts, can be tedious, time-consuming and infeasible for large datasets. We use autoregressive (AR) models for feature extraction and characterization of EEG signals containing several kinds of subject-generated artifacts. AR model parameters are scale-invariant features that can be used to develop models of artifacts across a population. We use a support vector machine (SVM) classifier to discriminate among artifact conditions using the AR model parameters as features. Results indicate reliable classification among several different artifact conditions across subjects (approximately 94%). These results suggest that AR modeling can be a useful tool for discriminating among artifact signals both within and across individuals.

Guidelines and Best Practices for Electrophysiological Data Collection, Analysis and Reporting in Autism

The EEG reflects the activation of large populations of neurons that act in synchrony and propagate to the scalp surface. This activity reflects both the brain’s background electrical activity and when the brain is being challenged by a task. Despite strong theoretical and methodological arguments for the use of EEG in understanding the neural correlates of autism, the practice of collecting, processing and evaluating EEG data is complex. Scientists should take into consideration both the nature of development in autism given the life-long, pervasive course of the disorder and the disability of altered or atypical social, communicative, and motor behaviors, all of which require accommodations to traditional EEG environments and paradigms. This paper presents guidelines for the recording, analyzing, and interpreting of EEG data with participants with autism. The goal is to articulate a set of scientific standards as well as methodological considerations that will increase the general field’s understanding of EEG methods, provide support for collaborative projects, and contribute to the evaluation of results and conclusions.

Hierarchical Event Descriptor (HED) tags for analysis of event-related EEG studies

Data from well-designed EEG experiments should find uses beyond initial reports, even when study authors cannot anticipate how it may contribute to future analyses. Several ontologies have been proposed for describing events in cognitive experiments to make data available for re-use and meta-analysis, but none are widely used. One reason for this is that the tools needed to make use of these ontologies are complex, placing a significant burden on experimenters while not providing any immediate reward for their efforts. Here we propose an extensible, user-friendly experiment event tagging method built on the BrainMap and CogPO ontologies and similar to the object tagging style used extensively on the Web. Hierarchical Event Descriptor (HED) tags, a hierarchy of standard and extended descriptors for EEG experimental events, provide a uniform human- and machine-readable interface facilitating use of an underlying event-description ontology during EEG data acquisition, analysis, and sharing. HED tags may be used to mark and annotate all known events in an experimental session. We describe an available real-time EEG experiment control and recording system that uses HED tags for annotation, transmission and storage of detailed information about events in EEG experiments.