Surface Laplacian of scalp electrical signals and independent component analysis resolve EMG contamination of electroencephalogram

The serious impact of electromyogram (EMG) contamination of electroencephalogram (EEG) is well recognised. The objective of this research is to demonstrate that combining independent component analysis with the surface Laplacian can eliminate EMG contamination of the EEG, and to validate that this processing does not degrade expected neurogenic signals. The method involves sequential application of ICA, using a manual procedure to identify and discard EMG components, followed by the surface Laplacian. The extent of decontamination is quantified by comparing processed EEG with EMG-free data that was recorded during pharmacologically induced neuromuscular paralysis. The combination of the ICA procedure and the surface Laplacian, with a flexible spherical spline, results in a strong suppression of EMG contamination at all scalp sites and frequencies. Furthermore, the ICA and surface Laplacian procedure does not impair the detection of well-known, cerebral responses; alpha activity with eyes-closed; ERP components (N1, P2) in response to an auditory oddball task; and steady state responses to photic and auditory stimulation. Finally, more flexible spherical splines increase the suppression of EMG by the surface Laplacian. We postulate this is due to ICA enabling the removal of local muscle sources of EMG contamination and the Laplacian transform being insensitive to distant (postural) muscle EMG contamination.     

Correction of blink artifacts using independent component analysis and empirical mode decomposition

Blink‐related ocular activity is a major source of artifacts in electroencephalogram (EEG) data. Independent component analysis (ICA) is a well‐known technique for the correction of such ocular artifacts, but one of the limitations of ICA is that the ICs selected for removal contain not only ocular activity but also some EEG activity. Straightforward removal of these ICs might, therefore, lead to a loss of EEG data. In this article a method is proposed to separate blink‐related ocular activity from actual EEG by combining ICA with a novel technique, empirical mode decomposition. This combination of two techniques allows for maximizing the retention of EEG data and the selective removal of the eyeblink artifact. The performance of the proposed method is demonstrated with simulated and real data.     

Validation of ICA as a tool to remove eye movement artifacts from EEG/ERP

Eye movement artifacts in electroencephalogram (EEG) recordings can greatly distort grand mean event‐related potential (ERP) waveforms. Different techniques have been suggested to remove these artifacts prior to ERP analysis. Independent component analysis (ICA) is suggested as an alternative method to “filter” eye movement artifacts out of the EEG, preserving the brain activity of interest and preserving all trials. However, the identification of artifact components is not always straightforward. Here, we compared eye movement artifact removal by ICA compiled on 10 s of EEG, on eye movement epochs, or on the complete EEG recording to the removal of eye movement artifacts by rejecting trials or by the Gratton and Coles method. ICA performed as well as the Gratton and Coles method. By selecting only eye movement epochs for ICA compilation, we were able to facilitate the identification of components representing eye movement artifacts.     

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

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

A method for detection of Alzheimer's disease using ICA-enhanced EEG measurements

OBJECTIVE: Many researchers have studied automatic EEG classification and recently a lot of work has been done on artefact-removal from EEG data using independent component analyses (ICA). However, demonstrating that a ICA-processed multichannel EEG measurement becomes more interpretable compared to the raw data (as is usually done in work on ICA-processing of EEG data) does not yet prove that detection of (incipient) anomalies is also better possible after ICA-processing. The objective of this study is to show that ICA-preprocessing is useful when constructing a detection system for Alzheimer's disease. METHODS AND MATERIAL: The paper describes a method for detection of EEG patterns indicative of Alzheimer's disease using automatic pattern recognition techniques. Our method incorporates an artefact removal stage based on ICA prior to automatic classification. The method is evaluated on measurements of a length of 8s from two groups of patients, where one group is in an initial stage of the disease (28 patients), whereas the other group is in a more progressed stage (15 patients). Both setups include a control group that should be classified as normal (10 and 21, respectively). RESULTS: Our final classification results for the group with severe Alzheimer's disease are comparable to the best results from literature. We show that ICA-based reduction of artefacts improves classification results for patients in an initial stage. CONCLUSION: We conclude that a more robust detection of Alzheimer's disease related EEG patterns may be obtained by employing ICA as ICA based pre-processing of EEG data can improve classification results for patients in an initial stage of Alzheimer's disease.     

MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition

We have developed an effective technique for extracting and classifying motor unit action potentials (MUAPs) for electromyography (EMG) signal decomposition. This technique is based on single-channel and short periodȁ9s real recordings from normal subjects and artificially generated recordings. This EMG signal decomposition technique has several distinctive characteristics compared with the former decomposition methods: (1) it bandpass filters the EMG signal through wavelet filter and utilizes threshold estimation calculated in wavelet transform for noise reduction in EMG signals to detect MUAPs before amplitude single threshold filtering; (2) it removes the power interference component from EMG recordings by combining independent component analysis (ICA) and wavelet filtering method together; (3) the similarity measure for MUAP clustering is based on the variance of the error normalized with the sum of RMS values for segments; (4) it finally uses ICA method to subtract all accurately classified MUAP spikes from original EMG signals. The technique of our EMG signal decomposition is fast and robust, which has been evaluated through synthetic EMG signals and real EMG signals.     

Relation of Gamma Oscillations in Scalp Recordings to Muscular Activity

We recorded scalp electrical activity before and after full neuro-muscular paralysis in 5 volunteers and determined differences due to elimination of muscular activity on several standard applications of EEG. Due to paralysis, there were reductions in ‘noisiness’ of the standard scalp recordings which were maximal over the peripheral scalp, not explained by abolition of movement artefact, and best accounted for by sustained EMG activity in resting individuals. There was a corresponding reduction in spectral power in the gamma range. In central leads, the extent of gamma frequency coherence during a non-time-locked mental task (1 s epochs) was reduced by paralysis, likely due to a reduction in gamma-frequency coherence in widely arising EMG signals. In a time-locked mental task (auditory oddball), evoked responses were qualitatively unaffected by paralysis but 3 of 4 induced gamma responses were obscured by EMG. This article is one of five on the “Special Topic: Discussing Gamma” in issue 22(1) of Brain Topography.     

Cortical Source Localization of Infant Cognition

Neuroimaging techniques such as positron emission topography (PET) and functional magnetic resonance imaging (fMRI) have been utilized with older children and adults to identify cortical sources of perceptual and cognitive processes. However, due to practical and ethical concerns, these techniques cannot be routinely applied to infant participants. An alternative to such neuroimaging techniques appropriate for use with infant participants is high-density electroencephalogram (EEG) recording and cortical source localization techniques. The current article provides an overview of a method developed for such analyses. The method consists of four steps: (1) recording high-density (e.g., 128-channel) EEG. (2) Analysis of individual participant raw segmented data with independent component analysis (ICA). (3) Estimation of equivalent current dipoles (ECDs) that represent cortical sources for the observed ICA component clusters. (4) Calculation of component activations in relation to experimental factors. We discuss an example of research applying this technique to investigate the development of visual attention and recognition memory. We also describe the application of “realistic head modeling” to address some of the current limitations of infant cortical source localization.     

Testing Multiple Psychological Processes for Common Neural Mechanisms Using EEG and Independent Component Analysis

Temporal independent component analysis (ICA) is applied to an electrophysiological signal mixture (such as an EEG recording) to disentangle the independent neural source signals—independent components—underlying said signal mixture. When applied to scalp EEG, ICA is most commonly used either as a pre-processing step (e.g., to isolate physiological processes from non-physiological artifacts), or as a data-reduction step (i.e., to focus on one specific neural process with increased signal-to-noise ratio). However, ICA can be used in an even more powerful way that fundamentally expands the inferential utility of scalp EEG. The core assumption of EEG-ICA—namely, that individual independent components represent separable neural processes—can be leveraged to derive the following inferential logic: If a specific independent component shows activity related to multiple psychological processes within the same dataset (e.g., elicited by different experimental events), it follows that those psychological processes involve a common, non-separable neural mechanism. As such, this logic allows testing a class of hypotheses that is beyond the reach of regular EEG analyses techniques, thereby crucially increasing the inferential utility of the EEG. In the current article, this logic will be referred to as the ‘common independent process identification’ (CIPI) approach. This article aims to provide a tutorial into the application of this powerful approach, targeted at researchers that have a basic understanding of standard EEG analysis. Furthermore, the article aims to exemplify the usage of CIPI by outlining recent studies that successfully applied this approach to test neural theories of mental functions.     

Properties of the ballistocardiogram artefact as revealed by EEG recordings at 1.5, 3 and 7 T static magnetic field strength.

Electroencephalogram (EEG) data recorded simultaneously with functional magnetic resonance imaging (fMRI) suffer from severe artefacts. The ballistocardiogram (BCG) artefact in particular is as yet poorly understood and different BCG removal strategies have been proposed. In the present study, EEG data were recorded from four participants in three different MRI scanners with field strengths of 1.5, 3 and 7 T, with the aim of investigating the impact of the static magnetic field strength on the BCG artefact and independent component analysis (ICA). The results confirm that the amplitude of the BCG artefact is a function of the static magnetic field strength. Moreover, the spatial variability of the BCG artefact substantially increased at higher magnetic field strengths. A comparison of ICA before and after channel-wise BCG correction revealed that typical independent components could be more easily identified when ICA was applied after channel-wise BCG correction. Further analysis of EEG and electrocardiogram recordings points towards the contribution of at least two different processes to the origin of the BCG, which are blood movement or axial head rotation on the one hand and electrode movement at lateral sites of the head on the other. This is summarized in a preliminary BCG model that may help to explain recent inconsistencies regarding the usefulness of ICA for BCG removal. It may also guide the future development of more advanced BCG removal procedures.     

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

Neuronal activity in the gamma‐band range was long considered a marker of object representation. However, scalp‐recorded EEG activity in this range is contaminated by a miniature saccade‐related muscle artifact. Independent component analysis (ICA) has been proposed as a method of removal of such artifacts. Alternatively, beamforming, a source analysis method in which potential sources of activity across the whole brain are scanned independently through the use of adaptive spatial filters, offers a promising method of accounting for the artifact without relying on its explicit removal. We present here the application of ICA‐based correction to a previously published dataset. Then, using beamforming, we examine the effect of ICA correction on the scalp‐recorded EEG signal and the extent to which genuine activity is recoverable before and after ICA correction. We find that beamforming attributes much of the scalp‐recorded gamma‐band signal before correction to deep frontal sources, likely the eye muscles, which generate the artifact related to each miniature saccade. Beamforming confirms that what is removed by ICA is predominantly this artifactual signal, and that what remains after correction plausibly originates in the visual cortex. Thus, beamforming allows researchers to confirm whether their removal procedures successfully removed the artifact. Our results demonstrate that ICA‐based correction brings about general improvements in signal‐to‐noise ratio suggesting it should be used along with, rather than be replaced by, beamforming.     

Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods.

A variety of procedures have been proposed to correct ocular artifacts in the electroencephalogram (EEG), including methods based on regression, principal components analysis (PCA) and independent component analysis (ICA). The current study compared these three methods, and it evaluated a modified regression approach using Bayesian adaptive regression splines to filter the electrooculogram (EOG) before computing correction factors. We applied each artifact correction procedure to real and simulated EEG data of varying epoch lengths and then quantified the impact of correction on spectral parameters of the EEG. We found that the adaptive filter improved regression-based artifact correction. An automated PCA method effectively reduced ocular artifacts and resulted in minimal spectral distortion, whereas ICA correction appeared to distort power between 5 and 20 Hz. In general, reducing the epoch length improved the accuracy of estimating spectral power in the alpha (7.5-12.5 Hz) and beta (12.5-19.5 Hz) bands, but it worsened the accuracy for power in the theta (3.5-7.5 Hz) band and distorted time domain features. Results supported the use of regression-based and PCA-based ocular artifact correction and suggested a need for further studies examining possible spectral distortion from ICA-based correction procedures.     

Multiple Response Comparison of Parietal EEG and Frontalis EMG Biofeedback

In Study I parietal EEG and frontalis EMG were simultaneously recorded from 20 normal subjects while half of the subjects received 45 min of eyes-closed alpha EEG enhancement feedback and the other half received a similar amount of EMG suppression feedback. EMG feedback resulted in a significant reduction in frontalis EMG activity accompanied by a reliable increase in parietal alpha density, while EEG feedback produced only an increase in alpha without corresponding EMG reduction. In Study II, each of 8 subjects underwent four separate feedback contingencies in two 40-min sessions—one session with eyes open and the other with eyes closed. The four types of feedback were: a) alpha-up (alpha enhancement), b) alpha-down (alpha suppression), c) EMG-down (frontalis tension decrease), and d) EMG-up (frontalis tension increase). EMG feedback, up and down, resulted in the more consistent pattern of generalized arousal changes reflected in heart rate and respiratory rate as well as EEG and EMG activity. Within the constraints of a limited training period, the results suggest that frontalis EMG feedback is the more efficient procedure for producing a generalized relaxation response. However, since fingertip vasoconstriction accompanied all four types of feedback, caution must be exercised to avoid the oversimplification of generalized organism effects.     

Removal of large muscle artifacts from transcranial magnetic stimulation-evoked EEG by independent component analysis

We present two techniques utilizing independent component analysis (ICA) to remove large muscle artifacts from transcranial magnetic stimulation (TMS)-evoked EEG signals. The first one is a novel semi-automatic technique, called enhanced deflation method (EDM). EDM is a modification of the deflation mode of the FastICA algorithm; with an enhanced independent component search, EDM is an effective tool for removing the large, spiky muscle artifacts. The second technique, called manual method (MaM) makes use of the symmetric mode of FastICA and the artifactual components are visually selected by the user. In order to evaluate the success of the artifact removal methods, four different quality parameters, based on curve comparison and frequency analysis, were studied. The dorsal premotor cortex (dPMC) and Broca’s area (BA) were stimulated with TMS. Both methods removed the very large muscle artifacts recorded after stimulation of these brain areas. However, EDM was more stable, less subjective, and thus also faster to use than MaM. Until now, examining lateral areas of the cortex with TMS—EEG has been restricted because of strong muscle artifacts. The methods described here can remove those muscle artifacts, allowing one to study lateral areas of the human brain, e.g., BA, with TMS—EEG.     

Muscle Artifact Removal from Human Sleep EEG by Using Independent Component Analysis

Muscle artifacts are typically associated with sleep arousals and awakenings in normal and pathological sleep, contaminating EEG recordings and distorting quantitative EEG results. Most EEG correction techniques focus on ocular artifacts but little research has been done on removing muscle activity from sleep EEG recordings. The present study was aimed at assessing the performance of four independent component analysis (ICA) algorithms (AMUSE, SOBI, Infomax, and JADE) to separate myogenic activity from EEG during sleep, in order to determine the optimal method. AMUSE, Infomax, and SOBI performed significantly better than JADE at eliminating muscle artifacts over temporal regions, but AMUSE was independent of the signal-to-noise ratio over non-temporal regions and markedly faster than the remaining algorithms. AMUSE was further successful at separating muscle artifacts from spontaneous EEG arousals when applied on a real case during different sleep stages. The low computational cost of AMUSE, and its excellent performance with EEG arousals from different sleep stages supports this ICA algorithm as a valid choice to minimize the influence of muscle artifacts on human sleep EEG recordings.     

Measurement of the extraocular spike potential during saccade countermanding

The stop signal task is used to investigate motor inhibition. Several groups have reported partial electromyogram (EMG) activation when subjects successfully withhold manual responses and have used this finding to define the nature of response inhibition properties in the spinal motor system. It is unknown whether subthreshold EMG activation from extraocular muscles can be detected in the saccadic response version of the stop signal task. The saccadic spike potential provides a way to examine extraocular EMG activation associated with eye movements in electroencephalogram (EEG) recordings. We used several techniques to isolate extraocular EMG activation from anterior electrode locations of EEG recorded from macaque monkeys. Robust EMG activation was present when eye movements were made, but no activation was detected when saccades were deemed canceled. This work highlights a key difference between the spinal motor system and the saccade system.     

Biopotentials as command and feedback signals in functional electrical stimulation systems

Today Functional Electrical Stimulation (FES) is available as a clinical tool in muscle activation used for picking up objects, for standing and walking, for controlling bladder emptying, and for breathing. Despite substantial progress in development and new knowledge, many challenges remain to be resolved to provide a more efficient functionality of FES systems. The most important task of these challenges is to improve control of the activated muscles through open loop or feedback systems. Command and feedback signals can be extracted from biopotentials recorded from muscles (Electromyogram, EMG), nerves (Electroneurogram, ENG), and the brain (Electroencephalogram (EEG) or individual cells). This paper reviews work in which EMG, ENG, and EEG signals in humans have been used as command and feedback signals in systems using electrical stimulation of motor nerves to restore movements after an injury to the Central Nervous System (CNS). It is concluded that the technology is ready to push for more substantial clinical FES investigations in applying muscle and nerve signals. Brain-computer interface systems hold great prospects, but require further development of faster and clinically more acceptable technologies.     

Removal of ocular artifacts from the EEG: a comparison between time-domain regression method and adaptive filtering method using simulated data

We recently proposed an adaptive filtering (AF) method for removing ocular artifacts from EEG recordings. The method employs two parameters: the forgetting factor λ and the filter length M. In this paper, we first show that when λ = M = 1, the adaptive filtering method becomes equivalent to the widely used time-domain regression method. The role of λ (when less than one) is to deal with the possible non-stationary relationship between the reference EOG and the EOG component in the EEG. To demonstrate the role of M, a simulation study is carried out that quantitatively evaluates the accuracy of the adaptive filtering method under different conditions and comparing with the accuracy of the regression method. The results show that when there is a shape difference or a misalignment between the reference EOG and the EOG artifact in the EEG, the adaptive filtering method can be more accurate in recovering the true EEG by using an M larger than one (e.g. M = 2 or 3).     

Blind Source Separation of Concurrent Disease-Related Patterns from EEG in Creutzfeldt–Jakob Disease for Assisting Early Diagnosis

Creutzfeldt–Jakob disease (CJD) is a rare, transmissible and fatal prion disorder of brain. Typical electroencephalography (EEG) patterns, such as the periodic sharp wave complexes (PSWCs), do not clearly emerge until the middle stage of CJD. To reduce transmission risks and avoid unnecessary treatments, the recognition of the hidden PSWCs forerunners from the contaminated EEG signals in the early stage is imperative. In this study, independent component analysis (ICA) was employed on the raw EEG signals recorded at the first admissions of five patients to segregate the co-occurrence of multiple disease-related features, which were difficult to be detected from the smeared EEG. Clear CJD-related waveforms, i.e., frontal intermittent rhythmical delta activity (FIRDA), fore PSWCs (triphasic waves) and periodic lateralized epileptiform discharges (PLEDs), have been successfully and simultaneously resolved from all patients. The ICA results elucidate the concurrent appearance of FIRDA and PLEDs or triphasic waves within the same EEG epoch, which has not been reported in the previous literature. Results show that ICA is an objective and effective means to extract the disease-related patterns for facilitating the early diagnosis of CJD.     

Kinking, coiling, and tortuosity of extracranial internal carotid artery: is it the effect of a metaplasia?

Introduction Morphological anomalies of the extracranial internal carotid artery (ICA) cause symptomatic cerebrovascular insufficiency in 4–16% of the cases. The aim of the present study is to evaluate macroscopic and microscopic features of a group of extracranial ICA anomalies, specifically kinking, coiling, and tortuosity, eventually affecting the surgical approach.Materials and methods From January 2003 to December 2005, 10 out of 169 (6%) revascularized patients (pts) were operated upon because of an ICA anomaly. They were all but two symptomatics. Seven pts were treated by ICA transection and end-to-side reimplantation of the ICA at the level of the carotid bulb; three pts were treated by ICA resection and end-to-end anastomosis. In all the cases a segment of ICA was resected; in three cases one more segment was also obtained from a common carotid artery (CCA) and these specimens were histologically examined. Patients were followed-up through a 3-year period.Results No pts died and none suffered of neurologic events. Duplex scan and arteriographic postoperative control showed the correct surgical reconstruction. Matching preoperative clinical findings with presence or absence of significant atherosclerotic stenotic lesion, we found out a positive cerebral CT in one pt (20%) in both groups; fluent neurological deficit was preeminent in pts with pure ICA anomalies (40% vs. 0%) (P = 0.2); pts with pure ICA anomalies were significantly younger than 65 years old (80% vs. 0%) (P = 0.03) and males were more involved by pure ICA anomalies (60% vs. 40%) (P = 0.1). The histological examination of ICA specimens showed a reduction of elastic fibers and muscular cells with a compensative increase of connective fibers.Conclusions At our knowledge this is the first study focused on ICA anomalies like kinking, coiling, and tortuosity, comparing histologic features of CCA and ICA specimens coming from the same affected carotid axis. Our results, although preliminary, show elastic and muscular tissue substituted by loose connective tissue, configuring a metaplasia of tunica media limited to the ICA. Our hypothesis is that extracranial ICA, being a segment of transition between an elastic vessel (CCA) and a muscular vessel (intracranial ICA), is particularly subject to metaplastic transformation, analogously to other transition zones in human body. Our purpose is now to confirm by ultrastructural and molecular biology techniques, in a wider series, the presence of this metaplasia, since this could condition also the revascularization techniques.