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

ERPs differentiate syllable and nonphonetic sound processing in children and adults

We examined maturation of speech-sound-related indices of auditory event-related brain potentials (ERPs). ERPs were elicited by syllables and nonphonetic correlates in children and adults. Compared with syllables, nonphonetic stimuli elicited larger N1 and P2 in adults and P1 in children. Because the nonphonetics were more perceptually salient, this N1 effect was consistent with known N1 sensitivity to sound onset features. Based on stimulus dependence and independent component structure, children's P1 appeared to contain overlapping P2-like activity. In both subject groups, syllables elicited larger N2/N4 peaks. This might reflect sound content feature processing, more extensive for speech than nonspeech sounds. Therefore, sound detection mechanisms (N1, P2) still develop whereas sound content processing (N2, N4) is largely mature during mid-childhood; in children and adults, speech sounds are processed more extensively than nonspeech sounds 200-400 ms poststimulus.

Correction of muscle artefacts in the EEG power spectrum

OBJECTIVE

To provide a method for correcting muscle artefacts in fast band power at EEG derivations.

METHODS

We define an indicator of surface EMG as power in the band 51.0-69.0 Hz ('muscle power'). This indicator is used to approximately eliminate the contribution of muscle activity on fast band power via a regression model.

RESULTS

(1) Patients show a larger proportion of muscle activity in fast band power. (2) There is a clear topographic pattern, frontal-temporal derivations being most susceptible to EMG artefacts. (3) The contribution of surface EMG can be drastically reduced by the proposed correction method. (4) Without correction, results for fast bands can be biased when e.g. comparing control and patient groups and the proposed correction method by and large eliminates this bias.

CONCLUSIONS

It is advisable to correct the quantitative EEG reflecting fast activity for the extent of EMG artefacts.

SIGNIFICANCE

To render the quantitative EEG more valid as an indicator of cerebral activity.

Testing the limits of the semantic illusion phenomenon: ERPs reveal temporary semantic change deafness in discourse comprehension

In general, language comprehension is surprisingly reliable. Listeners very rapidly extract meaning from the unfolding speech signal, on a word-by-word basis, and usually successfully. Research on 'semantic illusions' however suggests that under certain conditions, people fail to notice that the linguistic input simply doesn't make sense. In the current event-related brain potentials (ERP) study, we examined whether listeners would, under such conditions, spontaneously detect an anomaly in which a human character central to the story at hand (e.g., "a tourist") was suddenly replaced by an inanimate object (e.g., "a suitcase"). Because this replacement introduced a very powerful coherence break, we expected listeners to immediately notice the anomaly and generate the standard ERP effect associated with incoherent language, the N400 effect. However, instead of the standard N400 effect, anomalous words elicited a positive ERP effect from about 500-600 ms onwards. The absence of an N400 effect suggests that subjects did not immediately notice the anomaly, and that for a few hundred milliseconds the comprehension system has converged on an apparently coherent but factually incorrect interpretation. The presence of the later ERP effect indicates that subjects were processing for comprehension and did ultimately detect the anomaly. Therefore, we take the absence of a regular N400 effect as the online manifestation of a temporary semantic illusion. Our results also show that even attentive listeners sometimes fail to notice a radical change in the nature of a story character, and therefore suggest a case of short-lived 'semantic change deafness' in language comprehension.

EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease

OBJECTIVE

Development of an EEG preprocessing technique for improvement of detection of Alzheimer's disease (AD). The technique is based on filtering of EEG data using blind source separation (BSS) and projection of components which are possibly sensitive to cortical neuronal impairment found in early stages of AD.

METHODS

Artifact-free 20s intervals of raw resting EEG recordings from 22 patients with Mild Cognitive Impairment (MCI) who later proceeded to AD and 38 age-matched normal controls were decomposed into spatio-temporally decorrelated components using BSS algorithm 'AMUSE'. Filtered EEG was obtained by back projection of components with the highest linear predictability. Relative power of filtered data in delta, theta, alpha 1, alpha 2, beta 1, and beta 2 bands were processed with Linear Discriminant Analysis (LDA).

RESULTS

Preprocessing improved the percentage of correctly classified patients and controls computed with jack-knifing cross-validation from 59 to 73% and from 76 to 84%, correspondingly.

CONCLUSIONS

The proposed approach can significantly improve the sensitivity and specificity of EEG based diagnosis.

SIGNIFICANCE

Filtering based on BSS can improve the performance of the existing EEG approaches to early diagnosis of Alzheimer's disease. It may also have potential for improvement of EEG classification in other clinical areas or fundamental research. The developed method is quite general and flexible, allowing for various extensions and improvements.

Cortical and Subcortical Correlates of Electroencephalographic Alpha Rhythm Modulation

Neural correlates of electroencephalographic (EEG) alpha rhythm are poorly understood. Here, we related EEG alpha rhythm in awake humans to blood-oxygen-level-dependent (BOLD) signal change determined by functional magnetic resonance imaging (fMRI). Topographical EEG was recorded simultaneously with fMRI during an open versus closed eyes and an auditory stimulation versus silence condition. EEG was separated into spatial components of maximal temporal independence using independent component analysis. Alpha component amplitudes and stimulus conditions served as general linear model regressors of the fMRI signal time course. In both paradigms, EEG alpha component amplitudes were associated with BOLD signal decreases in occipital areas, but not in thalamus, when a standard BOLD response curve (maximum effect at ∼6 s) was assumed. The part of the alpha regressor independent of the protocol condition, however, revealed significant positive thalamic and mesencephalic correlations with a mean time delay of ∼2.5 s between EEG and BOLD signals. The inverse relationship between EEG alpha amplitude and BOLD signals in primary and secondary visual areas suggests that widespread thalamocortical synchronization is associated with decreased brain metabolism. While the temporal relationship of this association is consistent with metabolic changes occurring simultaneously with changes in the alpha rhythm, sites in the medial thalamus and in the anterior midbrain were found to correlate with short time lag. Assuming a canonical hemodynamic response function, this finding is indicative of activity preceding the actual EEG change by some seconds.

Electrophysiological evidence for cortical plasticity with movement repetition

The role of movement repetition and practice has been extensively studied as an aspect of motor skill learning but has rarely been investigated in its own right. As practice is considered a prerequisite for motor learning we expected that even the repetitive execution of a simple movement would rapidly induce changes in neural activations without changing performance. We used 64-channel event-related potential mapping to investigate these effects of movement repetition on corresponding brain activity in humans. Ten healthy right-handed young adults performed a power grip task under visual force control to ensure constant behaviour during the experimental session. The session consisted of two parts intersected by a break. For analysis each part was subdivided into two runs to control for potential attention or fatigue effects, which would be expected to disappear during the break. Microstate analysis revealed that distinct topographies and source configurations during movement preparation, movement execution and feedback integration are responsive to repetition. The observed patterns of changes differed for the three microstates, suggesting that different, repetition-sensitive neural mechanisms are involved. Moreover, this study clearly confirms that movement repetition, in the absence of skill learning, is capable of inducing changes in neural networks.

Fast robust subject-independent magnetoencephalographic source localization using an artificial neural network

We describe a system that localizes a single dipole to reasonable accuracy from noisy magnetoencephalographic (MEG) measurements in real time. At its core is a multilayer perceptron (MLP) trained to map sensor signals and head position to dipole location. Including head position overcomes the previous need to retrain the MLP for each subject and session. The training dataset was generated by mapping randomly chosen dipoles and head positions through an analytic model and adding noise from real MEG recordings. After training, a localization took 0.7 ms with an average error of 0.90 cm. A few iterations of a Levenberg-Marquardt routine using the MLP output as its initial guess took 15 ms and improved accuracy to 0.53 cm, which approaches the natural limit on accuracy imposed by noise. We applied these methods to localize single dipole sources from MEG components isolated by blind source separation and compared the estimated locations to those generated by standard manually assisted commercial software.

An improved P300-based brain-computer interface

A brain-computer interface (BCI) is a system for direct communication between brain and computer. The BCI developed in this work is based on a BCI described by Farwell and Donchin in 1988, which allows a subject to communicate one of 36 symbols presented on a 6 x 6 matrix. The system exploits the P300 component of event-related brain potentials (ERP) as a medium for communication. The processing methods distinguish this work from Donchin's work. In this work, independent component analysis (ICA) was used to separate the P300 source from the background noise. A matched filter was used together with averaging and threshold techniques for detecting the existence of P300s. The processing method was evaluated offline on data recorded from six healthy subjects. The method achieved a communication rate of 5.45 symbols/min with an accuracy of 92.1% compared to 4.8 symbols/min with an accuracy of 90% in Donchin's work. The online interface was tested with the same six subjects. The average communication rate achieved was 4.5 symbols/min with an accuracy of 79.5 % as apposed to the 4.8 symbols/min with an accuracy of 56 % in Donchin's work. The presented BCI achieves excellent performance compared to other existing BCIs, and allows a reasonable communication rate, while maintaining a low error rate.

Validation of SOBI components from high-density EEG

Second-order blind identification (SOBI) is a blind source separation (BSS) algorithm that can be used to decompose mixtures of signals into a set of components or putative recovered sources. Previously, SOBI, as well as other BSS algorithms, has been applied to magnetoencephalography (MEG) and electroencephalography (EEG) data. These BSS algorithms have been shown to recover components that appear to be physiologically and neuroanatomically interpretable. While some proponents of these algorithms suggest that fundamental discoveries about the human brain might be made through the application of these techniques, validation of BSS components has not yet received sufficient attention. Here we present two experiments for validating SOBI-recovered components. The first takes advantage of the fact that noise sources associated with individual sensors can be objectively validated independently from the SOBI process. The second utilizes the fact that the time course and location of primary somatosensory (SI) cortex activation by median nerve stimulation have been extensively characterized using converging imaging methods. In this paper, using both known noise sources and highly constrained and well-characterized neuronal sources, we provide validation for SOBI decomposition of high-density EEG data. We show that SOBI is able to (1) recover known noise sources that were either spontaneously occurring or artificially induced; (2) recover neuronal sources activated by median nerve stimulation that were spatially and temporally consistent with estimates obtained from previous EEG, MEG, and fMRI studies; (3) improve the signal-to-noise ratio (SNR) of somatosensory-evoked potentials (SEPs); and (4) reduce the level of subjectivity involved in the source localization process.

Event-related potential correlates of task switching and switch costs

Studies of task switching demonstrate that task switches are associated with response costs and that these costs are reduced when a cue is presented in advance of a switch. The present study examined cortical event-related potential correlates of task switching and switch costs in 39 participants during a cued match/mismatch discrimination task. Compared with non-switch trials, switch trials were associated with a larger cue-related, anticipatory P3b-like waveform. Switch trials were also associated with smaller target-related, stimulus-dependent P2 and P3-like components. Moreover, the switch-related amplitude variability in the P3b to the cue and the P2 to the target were associated with unique components of the residual switch costs. The results support an integrated model of task switching with complementary yet distinct roles for anticipatory and stimulus-dependent processes in task switching and switch costs.

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.

Using independent component analysis to remove artifact from electroencephalographic measured during stuttered speech

The electro-encephalographic (EEG) activity of people who stutter could provide invaluable information about the association of neural processing and stuttering. However, the EEG has never been adequately studied during speech in which stuttering naturally occurs. This is owing, in part, to the masking of the EEG signal by artifact from sources such as the speech musculature and from ocular activity. The aim of this paper was to demonstrate the ability of independent component analysis (ICA) to remove artifact from the EEG of stuttering children recorded while they are speaking and stuttering. The EEG of 16 male children who stuttered and 16 who did not stutter was recorded during a reading task. The recorded EEG that contained artifact was then subjected to ICA. The results demonstrated that the EEG assessed during stuttered speech had substantially more noise than the EEG of speech that did not contain stuttering (p < 0.01). Furthermore, it was shown that ICA could effectively remove this artifact in all 16 children (p < 0.01). The results from one child highlight the findings that ICA can be used to remove dominant artifact that has prevented the study of EEG activity during stuttered speech in children.

Refractoriness cannot explain why C-fiber laser-evoked brain potentials are recorded only if concomitant Adelta-fiber activation is avoided

Co-activation of Adelta- and C-fiber nociceptors by brief cutaneous laser heat stimuli may induce a dual sensation composed of first and second pain but evokes only a single, Adelta-fiber related, late laser-evoked potential (LEP). Yet, when concomitant activation of Adelta-nociceptors is avoided, C-nociceptor activation evokes an ultra-late LEP. As cumulating evidence indicates that late and ultra-late LEPs may share common generators, investigators have hypothesized that when Adelta-fibers trigger a late LEP, the later arriving C-fiber afferent volley cannot trigger an ultra-late LEP because underlying generators are in a 'refractory state'. Better understanding of these interactions could have important consequences regarding the functional significance of LEPs. Therefore, this hypothesis was tested by applying two consecutive laser stimuli to the hand dorsum such as to produce a second Adelta-nociceptor afferent volley arriving at generators during their expected 'refractory period'. Results showed that late LEPs evoked by the second stimulus were not altered and consequently that this hypothesis does not hold. In addition, when stimuli ended the sensory detection task, an ample P600 component was recorded. Studies have shown that this component is probably related to the P3b component described in other sensory modalities. This result provides support to the 'context closure' model hypothesizing that this component reflects the closure of information processing occurring when expectations are terminated. Altogether, these results suggest that late and ultra-late LEPs reflect very general processes, which are mainly related to detection and orientation and constitute only a fraction of the central processing of both nociceptive inputs.

Independent component analysis removing artifacts in ictal recordings

PURPOSE

Independent component analysis (ICA) is a novel algorithm able to separate independent components from complex signals. Studies in interictal EEG demonstrate its usefulness to eliminate eye, muscle, 50-Hz, electrocardiogram (ECG), and electrode artifacts. The goal of this study was to evaluate the usefulness of ICA in removing artifacts in ictal recordings with a known EEG onset.

METHODS

We studied 20 seizures of nine patients with focal epilepsy monitored in our video-EEG monitoring unit. ICA was applied to remove obvious artifacts in segments at the beginning of the seizure. The final EEGs were exported to the original format and were compared with the original EEG by two blinded examiners. We compared original recordings and the samples cleaned by digital filters (DFs), ICA and ICA plus digital filters (ICA + DFs), evaluating the possibility of finding an ictal pattern, the localization of the onset in area and time, and the global quality of the sample.

RESULTS

All the recordings except one (95%) improved after the use of ICA for the elimination of blinking and other artifacts. Three seizures were found in which in the original recordings did not permit us to detect an ictal pattern, and after ICA + DFs, an ictal onset was evident; in two of them, ICA alone was able to show this pattern. The best results in all the scores were obtained with ICA + DF. ICA was better than DFs. The agreement between the two reviewers was highly significant.

CONCLUSIONS

ICA is useful to remove artifacts from ictal recordings. When applied to ictal recordings, it increases the quality of the recording. In some cases, ICA may be useful to show ictal onsets obscured by artifacts. ICA + DFs obtained the best results regarding removal of the artifacts.

Advances in quantitative electroencephalogram analysis methods

Quantitative electroencephalogram (qEEG) plays a significant role in EEG-based clinical diagnosis and studies of brain function. In past decades, various qEEG methods have been extensively studied. This article provides a detailed review of the advances in this field. qEEG methods are generally classified into linear and nonlinear approaches. The traditional qEEG approach is based on spectrum analysis, which hypothesizes that the EEG is a stationary process. EEG signals are nonstationary and nonlinear, especially in some pathological conditions. Various time-frequency representations and time-dependent measures have been proposed to address those transient and irregular events in EEG. With regard to the nonlinearity of EEG, higher order statistics and chaotic measures have been put forward. In characterizing the interactions across the cerebral cortex, an information theory-based measure such as mutual information is applied. To improve the spatial resolution, qEEG analysis has also been combined with medical imaging technology (e.g., CT, MR, and PET). With these advances, qEEG plays a very important role in basic research and clinical studies of brain injury, neurological disorders, epilepsy, sleep studies and consciousness, and brain function.

Recurrence quantification analysis of sleep electoencephalogram in sleep apnea syndrome in humans

The aim of this study is to elucidate whether the results of recurrence quantification analysis (RQA) of sleep EEGs in sleep apnea syndrome are valuable for analyzing sleep EEGs in sleep apnea syndrome. We investigated the ability of RQA to discriminate sleep stages and to characterize the different behaviors of sleep EEGs in sleep apnea syndrome. RQA was applied to EEG signals during sleep stages 1, 2, slow wave sleep (SWS), REM and the stage 'awake.' The sleep EEG signals were obtained from the MIT-BIH polysomnographic database. To examine the differences in the RQA measures for all sleep stages, one-way analysis of variance (ANOVA) and post hoc analysis were performed. From the results, all sleep stages could be distinctly discriminated by means of the RQA measure of %RATIO. We observed that stage 1 and REM had fewer recurrences, and that stage 2 was more autocorrelated than the other stages. The different dynamic behaviors of wakefulness and sleep EEG were also observed. Of significant interest was the observation that RQA was able to distinguish stage 1 from REM. In conclusion, we suggest that the information obtained from RQA of sleep EEGs in sleep apnea syndrome is valuable for its analysis, and that RQA constitutes a useful tool for analyzing sleep EEGs in subjects with sleep apnea syndrome.

Haloperidol Impairs Learning and Error-related Negativity in Humans

Humans are able to monitor their actions for behavioral conflicts and performance errors. Growing evidence suggests that the error-related negativity (ERN) of the event-related cortical brain potential (ERP) may index the functioning of this response monitoring system and that the ERN may depend on dopaminergic mechanisms. We examined the role of dopamine in ERN and behavioral indices of learning by administering either 3 mg of the dopamine antagonist (DA) haloperidol (n = 17); 25 mg of diphenhydramine (n = 16), which has a similar CNS profile but without DA properties; or placebo (n = 18) in a randomized, double-blind manner to healthy volunteers. Three hours after drug administration, participants performed a go/no-go Continuous Performance Task, the Eriksen Flanker Task, and a learning-dependent Time Estimation Task. Haloperidol significantly attenuated ERN amplitudes recorded during the flanker task, impaired learning of time intervals, and tended to cause more errors of commission, compared to placebo, which did not significantly differ from diphenhydramine. Drugs had no significant effects on the stimulus-locked P1 and N2 ERPs or on behavioral response latencies, but tended to affect post-error reaction time (RT) latencies in opposite ways (haloperidol decreased and diphenhydramine increased RTs). These findings support the hypothesis that the DA system is involved in learning and the generation of the ERN.

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.

Removal of ocular artifacts from electro-encephalogram by adaptive filtering

The electro-encephalogram (EEG) is useful for clinical diagnosis and in biomedical research. EEG signals, however, especially those recorded from frontal channels, often contain strong electro-oculogram (EOG) artifacts produced by eye movements. Existing regression-based methods for removing EOG artifacts require various procedures for preprocessing and calibration that are inconvenient and time-consuming. The paper describes a method for removing ocular artifacts based on adaptive filtering. The method uses separately recorded vertical EOG and horizontal EOG signals as two reference inputs. Each reference input is first processed by a finite impulse response filter of length M (M = 3 in this application) and then subtracted from the original EEG. The method is implemented by a recursive least-squares algorithm that includes a forgetting factor (lambda = 0.9999 in this application) to track the non-stationary portion of the EOG signals. Results from experimental data demonstrate that the method is easy to implement and stable, converges fast and is suitable for on-line removal of EOG artifacts. The first three coefficients (up to M = 3) were significantly larger than any remaining coefficients.

Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals

OBJECTIVE

To propose a noise reduction procedure for magnetoencephalography (MEG) signals introducing an automatic detection system of artifactual components (ICs) separated by an independent component analysis (ICA) algorithm, and a control cycle on reconstructed cleaned data to recovery part of non-artifactual signals possibly lost by the blind mechanism.

METHODS

The procedure consisted of three main steps: (1) ICA for blind source separation (BSS); (2) automatic detection method of artifactual components, based on statistical and spectral ICs characteristics; (3) control cycle on 'discrepancy,' i.e. on the difference between original data and those reconstructed using only ICs automatically retained. Simulated data were generated as representative mixtures of some common brain frequencies, a source of internal Gaussian noise, power line interference, and two real artifacts (electrocardiogram=ECG, electrooculogram=EOG), with the adjunction of a matrix of Gaussian external noise. Three real data samples were chosen as representative of spontaneous noisy MEG data.

RESULTS

In simulated data the proposed set of markers selected three components corresponding to ECG, EOG and the Gaussian internal noise; in real-data examples, the automatic detection system showed a satisfactory performance in detecting artifactual ICs. 'Discrepancy' control cycle was redundant in simulated data, as expected, but it was a significant amelioration in two of the three real-data cases.

CONCLUSIONS

The proposed automatic detection approach represents a suitable strengthening and simplification of pre-processing data analyses. The proposed 'discrepancy' evaluation, after automatic pruning, seems to be a suitable way to render negligible the risk of loose non-artifactual activity when applying BSS methods to real data.

SIGNIFICANCE

The present noise reduction procedure, including ICA separation phase, automatic artifactual ICs selection and 'discrepancy' control cycle, showed good performances both on simulated and real MEG data. Moreover, application to real signals suggests the procedure to be able to separate different cerebral activity sources, even if characterized by very similar frequency contents.

Overview of artifact reduction and removal in evoked potential and event-related potential recordings

Artifact in one form or another needs to be contended with in all EEG and EP studies. Various methods have been employed to avoid, eliminate, or minimize artifact. This article has described the methods that have been available for some time and newer methods and their advantages. It is the authors' hope that through the use of these methods the accuracy of all EP and ERP measurements will be improved and promote the validity and general acceptance of EP and ERP recordings. Genuine and valuable data are contained in EPs and ERPs. The challenge is to extract relatively small voltage signals often occurring within a higher voltage background of artifact. The computing power required to perform the artifact removal/reduction procedures now is available with basic laptop and desktop computers, as are the software programs that provide the artifact removal/reduction capabilities. It may be of interest for a prudent researcher to integrate the currently available artifact rejection methods before subjecting the ERP and EP data for further analysis and subsequent publication.

A novel spatio-temporal decomposition of the EEG: derivation, validation and clinical application

OBJECTIVE

To obtain clinically useful graphical and numerical data on the distribution of activities in the EEG using a novel type of spatio-temporal decomposition.

METHODS

The EEG is divided into 1/4 s epochs. An approximation to the spatial distribution of the locally dominant activity in each epoch is represented as a point in a spatial component space. Points representing epochs dominated by activity from the same source form a cluster. The centres of these clusters represent the global spatial component of each source. As each spatial component is identified, its corresponding temporal activity is removed from the record, allowing activity from sources with smaller amplitude to become dominant in the reduced record. Successive components are identified in the reduced record. The method was applied to 40 normal EEGs and features were identified, which were common to them all. The method was also applied to 4 separate records with different forms of focal abnormality.

RESULTS

The method successfully separated components from the EEG representing alpha rhythm, eye artefact, electrode artefact and EEG. In 40 normal EEGs the method isolated spatial components that were common to all EEGs, and in 4 abnormal EEGs it achieved a high degree of mutual separation of alpha rhythm, focal spikes, focal theta and focal delta activities.

CONCLUSIONS

The method achieved a high degree of mutual separation of the EEG components and successfully differentiated the artefacts due to eye movement, ECG and electrode faults. The clinical implications are discussed.

Independent Component Analysis as a Tool to Eliminate Artifacts in EEG: A Quantitative Study

Independent component analysis (ICA) is a novel technique that calculates independent components from mixed signals. A hypothetical clinical application is to remove artifacts in EEG. The goal of this study was to apply ICA to standard EEG recordings to eliminate well-known artifacts, thus quantifying its efficacy in an objective way. Eighty samples of recordings with spikes and evident artifacts of electrocardiogram (EKG), eye movements, 50-Hz interference, muscle, or electrode artifact were studied. ICA components were calculated using the Joint Approximate Diagonalization of Eigen-matrices (JADE) algorithm. The signal was reconstructed excluding those components related to the artifacts. A normalized correlation coefficient was used as a measure of the changes caused by the suppression of these components. ICA produced an evident clearing-up of signals in all the samples. The morphology and the topography of the spike were very similar before and after the removal of the artifacts. The correlation coefficient showed that the rest of the signal did not change significantly. Two examiners independently looked at the samples to identify the changes in the morphology and location of the discharge and the artifacts. In conclusion, ICA proved to be a useful tool to clean artifacts in short EEG samples, without having the disadvantages associated with the digital filters. The distortion of the interictal activity measured by correlation analysis was minimal.

Quality of EEG in simultaneous EEG-fMRI for epilepsy

It is now possible to record the EEG continuously during fMRI studies. This is a very promising methodology that combines knowledge about neuronal activity and its metabolic response. The EEG recorded inside the fMRI scanner is, however,heavily contaminated by artifacts caused by the high intensity magnetic field and rapidly changing field gradients. Methods have been reported in the literature to reduce or eliminate these artifacts, in particular the ballistocardiogram and the artifact caused by currents induced by rapidly changing magnetic gradients. Nevertheless, recording the EEG simultaneously with fMRI remains an extremely delicate operation. In addition the use of artifact removal methods has only been reported by the laboratories in which they were developed. We report here the practical procedures we developed to reduce artifacts in a series of 10 epileptic patients, in the context of the visualization of epileptic spikes. We illustrate the effectiveness of methods designed to remove the scanning artifact and present new methods for removing the ballistocardiographic artifact. We present and evaluate techniques to obtain an EEG of good quality when performing simultaneous EEG and fMRI studies.

Independent components of magnetoencephalography: localization

We applied second-order blind identification (SOBI), an independent component analysis method, to MEG data collected during cognitive tasks. We explored SOBI's ability to help isolate underlying neuronal sources with relatively poor signal-to-noise ratios, allowing their identification and localization. We compare localization of the SOBI-separated components to localization from unprocessed sensor signals, using an equivalent current dipole modeling method. For visual and somatosensory modalities, SOBI preprocessing resulted in components that can be localized to physiologically and anatomically meaningful locations. Furthermore, this preprocessing allowed the detection of neuronal source activations that were otherwise undetectable. This increased probability of neuronal source detection and localization can be particularly beneficial for MEG studies of higher-level cognitive functions, which often have greater signal variability and degraded signal-to-noise ratios than sensory activation tasks.

Issues relating to the subtraction phase in EOG artefact correction of the EEG

An important method for removing the effect of ocular artefact from the EEG is 'EOG correction'. This method estimates the proportion of ocular artefact that is in the EEG, and removes it by subtraction. To date, EOG correction research has focused on problems relating to the estimation of the correction coefficients. Using both mathematical rationale and empirical data, this paper addresses issues relating to the subtraction phase, such as the magnitude of error that can be expected due to EOG correction. Using ERP methodology, principal component and regression analyses, it is shown that the N1P2 complex propagates forward to the horizontal and radial (but not vertical) electrooculograms (EOG), and it is shown mathematically that this will result in EOG-correction error. Assuming an accurate estimate of ocular contamination of the EEG, maximal subtraction-phase error of the N1P2 complex was found to be a prefrontal attenuation of 15-22%, decreasing to central and occipital enhancements of 3-4% and 13-14%, respectively. The magnitude of this subtraction-phase error is compared to between-subject ERP variability and to error associated with EOG rejection (omitting data contaminated by ocular artefact). It is argued that such EOG correction error is small relative to both artefact rejection procedures and to normal variability found in ERP studies, and that it is less pernicious than artefact rejection procedures.

Artifact correction of the ongoing EEG using spatial filters based on artifact and brain signal topographies

Review and analysis of continuous EEG recordings may be impeded by physiological artifacts such as blinks, eye movements, or cardiac activity. Spatial filters based on artifact and brain signal topographies can remove artifacts completely without distortion of relevant brain activity. The authors describe the basic principle of artifact correction by spatial filtering and they review different approaches to estimate artifact and brain signal topographies. The main focus is on the preselection approach, which is fast enough to be applied while paging through the segments of a digital EEG recording. Examples of real EEG segments, containing epileptic seizure activity or interictal spikes contaminated by artifacts, show that spatial filtering by preselection can be a useful tool during EEG review. Advantages and disadvantages of the different spatial filter approaches are discussed.

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.

Removal of ECG interference from the EEG recordings in small animals using independent component analysis

In experiments involving small animals, the electroencephalogram (EEG) recorded during severe injury and accompanying resuscitation exhibit the strong presence of electrocardiogram (ECG). For improved quantitative EEG (qEEG) analysis, it is therefore imperative to remove ECG interference from EEG. In this paper, we validate the use of independent component analysis (ICA) to effectively suppress the interference of ECG from EEG recordings during normal activity, asphyxia and recovery following asphyxia. Two channels of EEG from five rats were recorded continuously for 2 h. Simultaneous recording of one channel ECG was also made. Epochs of 4 s and 1 min were selected from baseline, asphyxia and recovery (every 10 min) and their independent components and power spectra were calculated. The improvement in normalized power spectrum of EEG obtained for all animals was 7.71+/-3.63 db at the 3rd minute of recovery and dropped to 1.15+/-0.60 db at 63rd minute. The application of ICA has been particularly useful when the power of EEG is low, such as that observed during early brain hypoxic-asphyxic injury. The method is also useful in situations where accurate indications of EEG signal power and frequency content are needed.

Imaging Brain Dynamics Using Independent Component Analysis

The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging (fMRI) data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain.

Event-related brain response abnormalities in autism: evidence for impaired cerebello-frontal spatial attention networks

Although under some conditions the attention-related late positive event-related potential (ERP) response (LPC) is apparently normal in autism during visual processing, the LPC elicited by visuospatial processing may be compromised. Results from this study provide evidence for abnormalities in autism in two components of the LPC generated during spatial processing. The early frontal distribution of the LPC which may reflect attention orienting was delayed or missing in autistic subjects during conditions in which attention was to peripheral visual fields. The later parietal distribution of the LPC which may be associated with context updating was smaller in amplitude in autistic subjects regardless of attention location. Both abnormalities suggest disruption of function in spatial attention networks in autism. Evidence that the cerebellar abnormalities in autism may underlie these deficits comes from: (1) similar results in ERP responses and spatial attention deficits in patients with cerebellar lesions; (2) brain-behavior correlations in normally functioning individuals associating the size of the posterior cerebellar vermis and the latency of the frontal LPC; and (3) a previously reported complementary correlation between the size of the posterior vermal lobules and spatial orienting speed. Although the scalp-recorded LPC is thought to be cortically generated, it may be modulated by subcortical neural activity. The cerebellum may serve as a modulating influence by affecting the task-related antecedent attentional process. The electrophysiological abnormalities reported here index spatial attention deficits in autism that may reflect cerebellar influence on both frontal and parietal spatial attention function.