Selection of independent components representing event-related brain potentials: a data-driven approach for greater objectivity

Following the development of increasingly precise measurement instruments and fine-grain analysis tools for electroencephalographic (EEG) data, analysis of single-trial event-related EEG has considerably widened the utility of this non-invasive method to investigate brain activity. Recently, independent component analysis (ICA) has become one of the most prominent techniques for increasing the feasibility of single-trial EEG. This blind source separation technique extracts statistically independent components (ICs) from the EEG raw signal. By restricting the signal analysis to those ICs representing the processes of interest, single-trial analysis becomes more flexible. Still, the selection-criteria for in- or exclusion of certain ICs are largely subjective and unstandardized, as is the actual selection process itself. We present a rationale for a bottom-up, data-driven IC selection approach, using clear-cut inferential statistics on both temporal and spatial information to identify components that significantly contribute to a certain event-related brain potential (ERP). With time-range being the only necessary input, this approach considerably reduces the pre-assumptions for IC selection and promotes greater objectivity of the selection process itself. To test the validity of the approach presented here, we present results from a simulation and re-analyze data from a previously published ERP experiment on error processing. We compare the ERP-based IC selections made by our approach to the selection made based on mere signal power. The comparison of ERP integrity, signal-to-noise ratio, and single-trial properties of the back-projected ICs outlines the validity of the approach presented here. In addition, functional validity of the extracted error-related EEG signal is tested by investigating whether it is predictive for subsequent behavioural adjustments.     

Single trial variability of EEG and fMRI responses to visual stimuli

Recent EEG-fMRI studies have suggested a novel method of data fusion which uses single trial (ST) estimates of event-related potentials in the fMRI analysis. This is potentially very powerful, but rests on the assumption that the ST variability observed in EEG is reflected in the fMRI signal. The current study investigated this assumption and compared two different data processing strategies for each modality. Five subjects underwent separate EEG and fMRI sessions with checkerboard stimuli at two contrasts. EEG data were preprocessed using wavelet denoising and independent component analysis (ICA), whilst the general linear model and ICA were used for fMRI. Amplitudes and latencies of the P1 and N2 components of the visual evoked potential (VEP) were calculated for each trial. For fMRI, the amplitudes and latencies of the ST haemodynamic responses (HR) were calculated. Within modality, the results for the two processing methods were significantly correlated in the majority of data sets. Across modality, the average amplitudes of the VEPs and HRs were also significantly correlated. Examination of ST variability demonstrated that the amplitudes of the mean VEPs and HRs are both influenced by the latency variability of the ST responses to a greater extent than the amplitude variability. For high contrast stimuli the latency variability in EEG and fMRI was significantly correlated, with a similar trend seen for the low contrast stimuli. The results confirm the validity of examining both the EEG and fMRI signals on an ST basis and suggest an underlying neuronal origin in both modalities.     

ICA-based spatiotemporal approach for single-trial analysis of postmovement MEG beta synchronization

The extraction of event-related oscillatory neuromagnetic activities from single-trial measurement is challenging due to the non-phase-locked nature and variability from trial to trial. The present study presents a method based on independent component analysis (ICA) and the use of a template-based correlation approach to extract Rolandic beta rhythm from magnetoencephalographic (MEG) measurements of right finger lifting. A single trial recording was decomposed into a set of coupled temporal independent components and corresponding spatial maps using ICA and the reactive beta frequency band for each trial identified using a two-spectrum comparison between the postmovement interval and a reference period. Task-related components survived dual criteria of high correlation with both the temporal and the spatial templates with an acceptance rate of about 80%. Phase and amplitude information for noise-free MEG beta activities were preserved not only for optimal calculation of beta rebound (event-related synchronization) but also for profound penetration into subtle dynamics across trials. Given the high signal-to-noise ratio (SNR) of this method, various methods of source estimation were used on reconstructed single-trial data and the source loci coherently anchored in the vicinity of the primary motor area. This method promises the possibility of a window into the intricate brain dynamics of motor control mechanisms and the cortical pathophysiology of movement disorder on a trial-by-trial basis.     

Localization of individual area neuronal activity

A family of methods, collectively known as independent component analysis (ICA), has recently been added to the array of methods designed to decompose a multi-channel signal into components. ICA methods have been applied to raw magnetoencephalography (MEG) and electroencephalography (EEG) signals to remove artifacts, especially when sources such as power line or cardiac activity generate strong components that dominate the signal. More recently, successful ICA extraction of stimulus-evoked responses has been reported from single-trial raw MEG and EEG signals. The extraction of weak components has often been erratic, depending on which ICA method is employed and even on what parameters are used. In this work, we show that if the emphasis is placed on individual "independent components," as is usually the case with standard ICA applications, differences in the results obtained for different components are exaggerated. We propose instead the reconstruction of regional brain activations by combining tomographic estimates of individual independent components that have been selected by appropriate spatial and temporal criteria. Such localization of individual area neuronal activity (LIANA) allows reliable semi-automatic extraction of single-trial regional activations from raw MEG data. We demonstrate the new method with three different ICA algorithms applied to both computer-generated signals and real data. We show that LIANA provides almost identical results with each ICA method despite the fact that each method yields different individual components.     

Single-trial variability in event-related BOLD signals

Most current analysis methods for fMRI data assume a priori knowledge of the time course of the hemodynamic response (HR) to experimental stimuli or events in brain areas of interest. In addition, they typically assume homogeneity of both the HR and the non-HR "noise" signals, both across brain regions and across similar experimental events. When HRs vary unpredictably, from area to area or from trial to trial, an alternative approach is needed. Here, we use Infomax independent component analysis (ICA) to detect and visualize variations in single-trial HRs in event-related fMRI data. Six subjects participated in four fMRI sessions each in which ten bursts of 8-Hz flickering-checkerboard stimulation were presented for 0.5-s (short) or 3-s (long) durations at 30-s intervals. Five axial slices were acquired by a Bruker 3-T magnetic resonance imager at interscan intervals of 500 ms (TR). ICA decomposition of the resulting blood oxygenation level-dependent (BOLD) data from each session produced an independent component active in primary visual cortex (V1) and, in several sessions, another active in medial temporal cortex (MT/V5). Visualizing sets of BOLD response epochs with novel BOLD-image plots demonstrated that component HRs varied substantially and often systematically across trials as well as across sessions, subjects, and brain areas. Contrary to expectation, in four of the six subjects the V1 component HR contained two positive peaks in response to short-stimulus bursts, while components with nearly identical regions of activity in long-stimulus sessions from the same subjects were associated with single-peaked HRs. Thus, ICA combined with BOLD-image visualization can reveal dramatic and unforeseen HR variations not apparent to researchers analyzing their data with event-related response averaging and fixed HR templates.     

Mapping single-trial EEG records on the cortical surface through a spatiotemporal modality

Event-related potentials (ERPs) induced by visual perception and cognitive tasks have been extensively studied in neuropsychological experiments. ERP activities time-locked to stimulus presentation and task performance are often observed separately at individual scalp channels based on averaged time series across epochs and experimental subjects. An analysis using averaged EEG dynamics could discount information regarding interdependency between ongoing EEG and salient ERP features. Advanced tools such as independent component analysis (ICA) have been developed for decomposing collections of single-trial EEG records into separate features. Those features (or independent components) can then be mapped onto the cortical surface using source localization algorithms to visualize brain activation maps and to study between-subject consistency. In this study, we propose a statistical framework for estimating the time course of spatiotemporally independent EEG components simultaneously with their cortical distributions. Within this framework, we implemented Bayesian spatiotemporal analysis for imaging the sources of EEG features on the cortical surface. The framework allows researchers to include prior knowledge regarding spatial locations as well as spatiotemporal independence of different EEG sources. The use of the Electromagnetic Spatiotemporal ICA (EMSICA) method is illustrated by mapping event-related EEG dynamics induced by events in a visual two-back continuous performance task. The proposed method successfully identified several interesting components with plausible corresponding cortical activation topographies, including processes contributing to the late positive complex (LPC) located in central parietal, frontal midline, and anterior cingulate cortex, to atypical mu rhythms associated with the precentral gyrus, and to the central posterior alpha activity in the precuneus.     

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms, Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data. We tested the upper bound performance of five methods for detecting various types of artifacts by separately optimizing and then applying them to artifact-free EEG data into which we had added simulated artifacts of several types, ranging in size from thirty times smaller (-50 dB) to the size of the EEG data themselves (0 dB). Of the methods tested, those involving spectral thresholding were most sensitive. Except for muscle artifact detection where we found no gain of using ICA, all methods proved more sensitive when applied to the ICA-decomposed data than applied to the raw scalp data: the mean performance for ICA was higher and situated at about two standard deviations away from the performance distribution obtained on raw data. We note that ICA decomposition also allows simple subtraction of artifacts accounted for by single independent components, and/or separate and direct examination of the decomposed non-artifact processes themselves.     

Cognitive neurophysiology: beyond averaging

Averaging of repeated responses to sensory stimuli is the standard approach in cognitive electrophysiology. This procedure can give rise to inappropriate interpretations in some situations, because two factors contribute to the average ERP responses: the amplitude of the responses during the individual experimental trials, and the concentration of the phases (phase-locking) across responses. Larger poststimulus single-trial amplitudes compared to prestimulus baseline are thought to correspond to a stimulus-related increase of postsynaptic potentials or/and activation of an increased amount of neural assemblies. But the functional interpretation of an enhanced inter-trial phase-locking is unclear. BOLD responses are probably related to single-trial EEG amplitudes, but not to the phase concentration across trials. Therefore, separation of amplitude and phase contributions is indispensable to avoid misinterpretations and to gain a deeper understanding of the relation between event-related EEG and fMRI.     

Independent component analysis of fMRI group studies by self-organizing clustering

Independent component analysis (ICA) is a valuable technique for the multivariate data-driven analysis of functional magnetic resonance imaging (fMRI) data sets. Applications of ICA have been developed mainly for single subject studies, although different solutions for group studies have been proposed. These approaches combine data sets from multiple subjects into a single aggregate data set before ICA estimation and, thus, require some additional assumptions about the separability across subjects of group independent components. Here, we exploit the application of similarity measures and a related visual tool to study the natural self-organizing clustering of many independent components from multiple individual data sets in the subject space. Our proposed framework flexibly enables multiple criteria for the generation of group independent components and their random-effects evaluation. We present real visual activation fMRI data from two experiments, with different spatiotemporal structures, and demonstrate the validity of this framework for a blind extraction and selection of meaningful activity and functional connectivity group patterns. Our approach is either alternative or complementary to the group ICA of aggregated data sets in that it exploits commonalities across multiple subject-specific patterns, while addressing as much as possible of the intersubject variability of the measured responses. This property is particularly of interest for a blind group and subgroup pattern extraction and selection.     

Spatial attention boosts short-latency neural responses in human visual cortex

In a previous study of visual-spatial attention, Martinez et al. (2007) replicated the well-known finding that stimuli at attended locations elicit enlarged early components in the averaged event-related potential (ERP), which were localized to extrastriate visual cortex. The mechanisms that underlie these attention-related ERP modulations in the latency range of 80-200 ms, however, remain unclear. The main question is whether attention produces increased ERP amplitudes in time-domain averages by augmenting stimulus-triggered neural activity, or alternatively, by increasing the phase-locking of ongoing EEG oscillations to the attended stimuli. We compared these alternative mechanisms using Morlet wavelet decompositions of event-related EEG changes. By analyzing single-trial spectral amplitudes in the theta (4-8 Hz) and alpha (8-12 Hz) bands, which were the dominant frequencies of the early ERP components, it was found that stimuli at attended locations elicited enhanced neural responses in the theta band in the P1 (88-120 ms) and N1 (148-184 ms) latency ranges that were additive with the ongoing EEG. In the alpha band there was evidence for both increased additive neural activity and increased phase-synchronization of the EEG following attended stimuli, but systematic correlations between pre- and post-stimulus alpha activity were more consistent with an additive mechanism. These findings provide the strongest evidence to date in humans that short-latency neural activity elicited by stimuli within the spotlight of spatial attention is boosted or amplified at early stages of processing in extrastriate visual cortex.     

Fluctuations in electrodermal activity reveal variations in single trial brain responses to painful laser stimuli--a fMRI/EEG study

Pain is a complex experience with sensory, emotional and cognitive aspects. It also includes a sympathetic response that can be captured by measuring the electrodermal activity (EDA). The present study was performed to investigate which brain areas are associated with sympathetic activation in experimental pain; an issue that has not been addressed with fMRI (functional magnetic resonance imaging) thus far. Twelve healthy subjects received painful laser stimulation to the left hand. The event-related fMRI BOLD (blood oxygen level dependent) response was measured together with simultaneous EEG (electroencephalography) and EDA recordings. Laser stimuli induced the expected EDA response, evoked EEG potentials and BOLD responses. Single trial EDA amplitudes were used to guide further analysis of fMRI and EEG data. We found significantly higher BOLD responses in trials with high EDA vs. low EDA trials, predominantly in the insula and somatosensory cortex (S1/S2). Likewise, in the EEG we found the N2 laser evoked potentials to have significantly higher amplitudes in trials with high vs. low EDA. Furthermore EDA-informed BOLD modeling explained additional signal variance in sensory areas and yielded higher group level activation. We conclude that the sympathetic response to pain is associated with activation in pain-processing brain regions, predominantly in sensory areas and that single trial (EDA)-information can add to BOLD modeling by taking some of the response variability across trials and subjects into account. Thus, EDA is a useful additional, objective index when pain is studied with fMRI/EEG which might be of particular relevance in the context of genetic- and pharmacoimaging.     

Frontal midline EEG dynamics during working memory

We show that during visual working memory, the electroencephalographic (EEG) process producing 5-7 Hz frontal midline theta (fmtheta) activity exhibits multiple spectral modes involving at least three frequency bands and a wide range of amplitudes. The process accounting for the fmtheta increase during working memory was separated from 71-channel data by clustering on time/frequency transforms of components returned by independent component analysis (ICA). Dipole models of fmtheta component scalp maps were consistent with their generation in or near dorsal anterior cingulate cortex. From trial to trial, theta power of fmtheta components varied widely but correlated moderately with theta power in other frontal and left temporal processes. The weak mean increase in frontal midline theta power with increasing memory load, produced entirely by the fmtheta components, largely reflected progressively stronger theta activity in a relatively small proportion of trials. During presentations of letter series to be memorized or ignored, fmtheta components also exhibited 12-15 Hz low-beta activity that was stronger during memorized than during ignored letter trials, independent of letter duration. The same components produced a brief 3-Hz burst 500 ms after onset of the Probe letter following each letter sequence. A new decomposition method, log spectral ICA, applied to normalized log time/frequency transforms of fmtheta component Memorize-letter trials, showed that their low-beta activity reflected harmonic energy in continuous, sharp-peaked theta wave trains as well as independent low-beta bursts. Possibly, the observed fmtheta process variability may index dynamic adjustments in medial frontal cortex to trial-specific behavioral context and task demands.     

Systematic latency variation of the auditory evoked M100: from average to single-trial data

Standard analyses of neurophysiologically evoked response data rely on signal averaging across many epochs associated with specific events. The amplitudes and latencies of these averaged events are subsequently interpreted in the context of the given perceptual, motor, or cognitive tasks. Can such critical timing properties of event-related responses be recovered from single-trial data? Here, we make use of the M100 latency paradigm used in previous magnetoencephalography (MEG) research to evaluate a novel single-trial analysis approach. Specifically, the latency of the auditory evoked M100 varies systematically with stimulus frequency over a well-defined time range (lower frequencies, e.g., 125 Hz, yield up to 25 ms longer latencies than higher frequencies, e.g., 1000 Hz). Here, we show that the complex filtering approach to single-trial analysis recovers this key characteristic of the M100 response, as well as some other important response properties relating to lateralization. The results illustrate (i) the utility of the complex filtering method and (ii) the potential of the M100 latency to be used for stimulus encoding, since the relevant variation can be observed in single trials.     

Estimating Granger causality after stimulus onset: a cautionary note

How the brain processes sensory input to produce goal-oriented behavior is not well-understood. Advanced data acquisition technology in conjunction with novel statistical methods holds the key to future progress in this area. Recent studies have applied Granger causality to multivariate population recordings such as local field potential (LFP) or electroencephalography (EEG) in event-related paradigms. The aim is to reveal the detailed time course of stimulus-elicited information transaction among various sensory and motor cortices. Presently, interdependency measures like coherence and Granger causality are calculated on ongoing brain activity obtained by removing the average event-related potential (AERP) from each trial. In this paper we point out the pitfalls of this approach in light of the inevitable occurrence of trial-to-trial variability of event-related potentials in both amplitudes and latencies. Numerical simulations and experimental examples are used to illustrate the ideas. Special emphasis is placed on the important role played by single trial analysis of event-related potentials in experimentally establishing the main conclusion.     

Single-trial EEG dynamics of object and face visual processing

There has been extensive work using early event-related potentials (ERPs) to study visual object processing. ERP analyses focus traditionally on mean amplitude differences, with the implicit assumption that all of the neuronal activity of interest is evoked by the stimulus in a time-locked manner from trial to trial. However, several recent studies have suggested that visual ERP components might be explained to a large extent by the partial phase resetting of ongoing activity in restricted frequency bands. Here we apply that approach to the neural processing of visual objects. We examine the single-trial dynamics of the EEG signal elicited by the presentation of noise textures, houses and faces. We show that the brain response to those stimuli is best explained by amplitude increase that is maximal in the 5- to 15-Hz frequency band. The results indicate also the presence of a substantial increase in phase coherence in the same frequency band. However, analyses of residual activity, after subtracting the mean from single trials, show that this increase in phase coherence is not due to phase resetting per se, but rather to the presence of the ERP+noise in each trial. In keeping with this idea, a simulation demonstrates that a purely evoked model of the ERP produces quantitatively very similar results. Finally, the stronger response to faces compared to other objects (the 'N170 face effect') can be explained by a pure modulation of amplitude centered in the 5- to 15-Hz band.     

ICA-based procedures for removing ballistocardiogram artifacts from EEG data acquired in the MRI scanner

Electroencephalogram (EEG) data acquired in the MRI scanner contains significant artifacts, one of the most prominent of which is ballistocardiogram (BCG) artifact. BCG artifacts are generated by movement of EEG electrodes inside the magnetic field due to pulsatile changes in blood flow tied to the cardiac cycle. Independent Component Analysis (ICA) is a statistical algorithm that is useful for removing artifacts that are linearly and independently mixed with signals of interest. Here, we demonstrate and validate the usefulness of ICA in removing BCG artifacts from EEG data acquired in the MRI scanner. In accordance with our hypothesis that BCG artifacts are physiologically independent from EEG, it was found that ICA consistently resulted in five to six independent components representing the BCG artifact. Following removal of these components, a significant reduction in spectral power at frequencies associated with the BCG artifact was observed. We also show that our ICA-based procedures perform significantly better than noise-cancellation methods that rely on estimation and subtraction of averaged artifact waveforms from the recorded EEG. Additionally, the proposed ICA-based method has the advantage that it is useful in situations where ECG reference signals are corrupted or not available.     

Dissociable medial frontal negativities from a common monitoring system for self- and externally caused failure of goal achievement

Goal-directed behavior requires the ability to adapt performance strategies based on the attribution of unintended outcomes to internal or external causes. Using event-related brain potentials, the present research compared neural activity following self-generated errors induced by a flanker task and following externally generated errors induced by supposed “technical malfunctions”. Errors and malfunctions were associated with temporally dissociable ERP components, the short-latency error-related negativity (ERN) and the longer-latency feedback-related negativity (FRN), respectively. Independent component analysis (ICA) was applied to compare the underlying neural components of ERN and FRN. ICA results revealed that the FRN is attributable to the neural sources of the ERN, suggesting that the two components share a source network. Furthermore, single-trial amplitudes of ERN and FRN were specifically related to the implementation of error correction and malfunction compensation: the stronger the failure signal, the more efficient was remedial behavior. Together the results demonstrate that internally and externally generated unintended action outcomes engage a common monitoring mechanism that manifests in two temporally distinct ERP components and induces similar compensatory processes. The temporal dissociation of the ERP components might provide the basis for further processes of outcome attribution underlying action selection and changes in performance strategy. In line with recent neuroimaging findings, ERN and FRN appear to reflect the use of different sources of information about action outcome to update action value.     

Latency (in)sensitive ICA. Group independent component analysis of fMRI data in the temporal frequency domain

Independent component analysis (ICA), a data-driven approach utilizing high-order statistical moments to find maximally independent sources, has found fruitful application in functional magnetic resonance imaging (fMRI). A limitation of the standard fMRI ICA model is that a given component's time course is required to have the same delay at every voxel. As spatially varying delays (SVDs) may be found in fMRI data, using an ICA model with a fixed temporal delay for each source will have two implications. Larger SVDs can result in the splitting of regions with different delays into different components. Second, smaller SVDs can result in a biased ICA amplitude estimate due to only a slight delay difference. We propose a straightforward approach for incorporating this prior temporal information and removing the limitation of a fixed source delay by performing ICA on the amplitude spectrum of the original fMRI data (thus removing latency information). A latency map is then estimated for each component using the resulting component images and the raw data. We show that voxels with similar time courses, but different delays, are grouped into the same component. Additionally, when using traditional ICA, the amplitudes of motor areas are diminished due to systematic delay differences between visual and motor areas. The amplitudes are more accurately estimated when using a latency-insensitive ICA approach. The resulting time courses, the component maps, and the latency maps may prove useful as an addition to the collection of methods for fMRI data analysis.