A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI

Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings

We propose a set of algorithms for sequentially removing artifacts related to MRI gradient switching and cardiac pulsations from electroencephalography (EEG) data recorded during functional magnetic resonance imaging (fMRI). Special emphasis is directed upon the use of statistical metrics and methods for the extraction and selection of features that characterize gradient and pulse artifacts. To remove gradient artifacts, we use channel-wise filtering based on singular value decomposition (SVD). To remove pulse artifacts, we first decompose data into temporally independent components and then select a compact cluster of components that possess sustained high mutual information with the electrocardiogram (ECG). After the removal of these components, the time courses of remaining components are filtered by SVD to remove the temporal patterns phase-locked to the cardiac timing markers derived from the ECG. The filtered component time courses are then inversely transformed into multi-channel EEG time series free of pulse artifacts. Evaluation based on a large set of simultaneous EEG-fMRI data obtained during a variety of behavioral tasks, sensory stimulations and resting conditions showed excellent data quality and robust performance attainable with the proposed methods. These algorithms have been implemented as a Matlab-based toolbox made freely available for public access and research use.

Multimodal imaging reveals the role of γ activity in eating-reflex seizures

In reflex epilepsies, alteration of γ oscillations may mediate transition between interictal and ictal states. Here, we explored a patient having seizures triggered by syrup intake. From intracranial electroencephalography combined with functional MRI, the overlap of the gustatory cortex and of the preictal and ictal onset zones, as defined by early gamma changes, motivated the successful resective surgery of the middle short gyrus of the right insula. This case provides a rare demonstration from human gamma activity that the route to seizure may be supported by the interplay between physiological and epileptogenic networks.

With or without spikes: localization of focal epileptic activity by simultaneous electroencephalography and functional magnetic resonance imaging

In patients with medically refractory focal epilepsy who are candidates for epilepsy surgery, concordant non-invasive neuroimaging data are useful to guide invasive electroencephalographic recordings or surgical resection. Simultaneous electroencephalography and functional magnetic resonance imaging recordings can reveal regions of haemodynamic fluctuations related to epileptic activity and help localize its generators. However, many of these studies (40-70%) remain inconclusive, principally due to the absence of interictal epileptiform discharges during simultaneous recordings, or lack of haemodynamic changes correlated to interictal epileptiform discharges. We investigated whether the presence of epilepsy-specific voltage maps on scalp electroencephalography correlated with haemodynamic changes and could help localize the epileptic focus. In 23 patients with focal epilepsy, we built epilepsy-specific electroencephalographic voltage maps using averaged interictal epileptiform discharges recorded during long-term clinical monitoring outside the scanner and computed the correlation of this map with the electroencephalographic recordings in the scanner for each time frame. The time course of this correlation coefficient was used as a regressor for functional magnetic resonance imaging analysis to map haemodynamic changes related to these epilepsy-specific maps (topography-related haemodynamic changes). The method was first validated in five patients with significant haemodynamic changes correlated to interictal epileptiform discharges on conventional analysis. We then applied the method to 18 patients who had inconclusive simultaneous electroencephalography and functional magnetic resonance imaging studies due to the absence of interictal epileptiform discharges or absence of significant correlated haemodynamic changes. The concordance of the results with subsequent intracranial electroencephalography and/or resection area in patients who were seizure free after surgery was assessed. In the validation group, haemodynamic changes correlated to voltage maps were similar to those obtained with conventional analysis in 5/5 patients. In 14/18 patients (78%) with previously inconclusive studies, scalp maps related to epileptic activity had haemodynamic correlates even when no interictal epileptiform discharges were detected during simultaneous recordings. Haemodynamic changes correlated to voltage maps were spatially concordant with intracranial electroencephalography or with the resection area. We found better concordance in patients with lateral temporal and extratemporal neocortical epilepsy compared to medial/polar temporal lobe epilepsy, probably due to the fact that electroencephalographic voltage maps specific to lateral temporal and extratemporal epileptic activity are more dissimilar to maps of physiological activity. Our approach significantly increases the yield of simultaneous electroencephalography and functional magnetic resonance imaging to localize the epileptic focus non-invasively, allowing better targeting for surgical resection or implantation of intracranial electrode arrays.

Characterization of the hemodynamic modes associated with interictal epileptic activity using a deformable model-based analysis of combined EEG and functional MRI recordings

Simultaneous electroencephalography and functional magnetic resonance imaging (EEG/fMRI) have been proposed to contribute to the definition of the epileptic seizure onset zone. Following interictal epileptiform discharges, one usually assumes a canonical hemodynamic response function (HRF), which has been derived from fMRI studies in healthy subjects. However, recent findings suggest that the hemodynamic properties of the epileptic brain are likely to differ significantly from physiological responses. Here, we propose a simple and robust approach that provides HRFs, defined as a limited set of gamma functions, optimized so as to elicit strong activations after standard model-driven statistical analysis at the single subject level. The method is first validated on healthy subjects using experimental data acquired during motor, visual and memory encoding tasks. Second, interictal EEG/fMRI data measured in 10 patients suffering from epilepsy are analyzed. Results show dramatic changes of activation patterns, depending on whether physiological or pathological assumptions are made on the hemodynamics of the epileptic brain. Our study suggests that one cannot assume a priori that HRFs in epilepsy are similar to the canonical model. This may explain why a significant fraction of EEG/fMRI exams in epileptic patients are inconclusive after standard data processing. The heterogeneous perfusion in epileptic regions indicates that the properties of brain vasculature in epilepsy deserve careful attention.

Theory and developments in an unobtrusive cardiovascular system representation: ballistocardiography

Due to recent technological improvements, namely in the field of piezoelectric sensors, ballistocardiography - an almost forgotten physiological measurement - is now being object of a renewed scientific interest.Transcending the initial purposes of its development, ballistocardiography has revealed itself to be a useful informative signal about the cardiovascular system status, since it is a non-intrusive technique which is able to assess the body's vibrations due to its cardiac, and respiratory physiological signatures.Apart from representing the outcome of the electrical stimulus to the myocardium - which may be obtained by electrocardiography - the ballistocardiograph has additional advantages, as it can be embedded in objects of common use, such as a bed or a chair. Moreover, it enables measurements without the presence of medical staff, factor which avoids the stress caused by medical examinations and reduces the patient's involuntary psychophysiological responses.Given these attributes, and the crescent number of systems developed in recent years, it is therefore pertinent to revise all the information available on the ballistocardiogram's physiological interpretation, its typical waveform information, its features and distortions, as well as the state of the art in device implementations.

Electromyogenic Artifacts and Electroencephalographic Inferences Revisited

Recent years have witnessed a renewed interest in using oscillatory brain electrical activity to understand the neural bases of cognition and emotion. Electrical signals originating from pericranial muscles represent a profound threat to the validity of such research. Recently, McMenamin et al (2010) examined whether independent component analysis (ICA) provides a sensitive and specific means of correcting electromyogenic (EMG) artifacts. This report sparked the accompanying commentary (Olbrich, Jödicke, Sander, Himmerich & Hegerl, in press), and here we revisit the question of how EMG can alter inferences drawn from the EEG and what can be done to minimize its pernicious effects. Accordingly, we briefly summarize salient features of the EMG problem and review recent research investigating the utility of ICA for correcting EMG and other artifacts. We then directly address the key concerns articulated by Olbrich and provide a critique of their efforts at validating ICA. We conclude by identifying key areas for future methodological work and offer some practical recommendations for intelligently addressing EMG artifact.

Adaptive removal of gradients-induced artefacts on ECG in MRI: a performance analysis of RLS filtering

One of the main vital signs used in patient monitoring during Magnetic Resonance Imaging (MRI) is Electro-Cardio-Gram (ECG). Unfortunately, magnetic fields gradients induce artefacts which severely affect ECG quality. Adaptive Noise Cancelling (ANC) is one of the preferred techniques for artefact removal. ANC involves the adaptive estimation of the impulse response of the system constituted by the MRI equipment, the patient and the ECG recording device. Least Mean Square (LMS) adaptive filtering has been traditionally employed because of its simplicity: anyway, it requires the choice of a step-size parameter, whose proper value for the specific application must be estimated case by case: an improper choice could yield slow convergence and unsatisfactory behaviour. Recursive Least Square (RLS) algorithm has, potentially, faster convergence while not requiring any parameter. As far as the authors' knowledge, there is no systematic analysis of performances of RLS in this scenario. In this study we evaluated the performance of RLS for adaptive removal of artefacts induced by magnetic field gradients on ECG in MRI, in terms of efficacy of suppression. Tests have been made on real signals, acquired via an expressly developed system. A comparison with LMS was made on the basis of opportune performance indices. Results indicate that RLS is superior to LMS in several respects.

Effect of the static magnetic field of the MR-scanner on ERPs: evaluation of visual, cognitive and motor potentials

OBJECTIVE

This work investigates the influence of the static magnetic field of the MR-scanner on ERPs extracted from simultaneous EEG-fMRI recordings. The quality of the ERPs after BallistoCardioGraphic (BCG) artifact removal, as well as the reproducibility of the waveforms in different environments is investigated.

METHODS

We consider a Detection, a Go-Nogo and a Motor task, eliciting peaks that differ in amplitude, latency and scalp topography, repeated in two situations: outside the scanner room (0T) and inside the MR-scanner but without gradients (3T). The BCG artifact is removed by means of three techniques: the Average Artifact Subtraction (AAS) method, the Optimal Basis Set (OBS) method and the Canonical Correlation Analysis (CCA) approach.

RESULTS

The performance of the three methods depends on the amount of averaged trials. Moreover, differences are found on both amplitude and latency of ERP components recorded in two environments (0T vs 3T).

CONCLUSIONS

We showed that, while ERPs can be extracted from simultaneous EEG-fMRI data at 3T, the static magnetic field might affect the physiological processes under investigation.

SIGNIFICANCE

The reproducibility of the ERPs in different recording environments (0T vs 3T) is a relevant issue that deserves further investigation to clarify the equivalence of cognitive processes in both behavioral and imaging studies.

Removal of BCG artifacts from EEG recordings inside the MR scanner: a comparison of methodological and validation-related aspects

Multimodal approaches are of growing interest in the study of neural processes. To this end much attention has been paid to the integration of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data because of their complementary properties. However, the simultaneous acquisition of both types of data causes serious artifacts in the EEG, with amplitudes that may be much larger than those of EEG signals themselves. The most challenging of these artifacts is the ballistocardiogram (BCG) artifact, caused by pulse-related electrode movements inside the magnetic field. Despite numerous efforts to find a suitable approach to remove this artifact, still a considerable discrepancy exists between current EEG-fMRI studies. This paper attempts to clarify several methodological issues regarding the different approaches with an extensive validation based on event-related potentials (ERPs). More specifically, Optimal Basis Set (OBS) and Independent Component Analysis (ICA) based methods were investigated. Their validation was not only performed with measures known from previous studies on the average ERPs, but most attention was focused on task-related measures, including their use on trial-to-trial information. These more detailed validation criteria enabled us to find a clearer distinction between the most widely used cleaning methods. Both OBS and ICA proved to be able to yield equally good results. However, ICA methods needed more parameter tuning, thereby making OBS more robust and easy to use. Moreover, applying OBS prior to ICA can optimize the data quality even more, but caution is recommended since the effect of the additional ICA step may be strongly subject-dependent.

Sources of non-physiologic noise in simultaneous EEG-fMRI data: a phantom study

Simultaneous EEG-fMRI studies require an understanding of the noise characteristics of the acquisition environment so that appropriate pre-processing steps may be taken to remove known artifacts from the data stream. Using a phantom approach, we have developed a general methodology for characterizing non-physiologic noise in EEG signal and demonstrate the use of this methodology for a specific MR scanner and EEG data acquisition system configuration. Our results show the δ frequency band is significantly impacted by baseline drift or baseline correction algorithms while the β and γ bands are impacted by residual gradient artifact and gradient corrections.

Good practices in EEG-MRI: the utility of retrospective synchronization and PCA for the removal of MRI gradient artefacts

The electroencephalogram (EEG) recorded during magnetic resonance imaging (MRI) inside the scanner is obstructed by the MRI gradient artefact (MGA) originating from the electromagnetic interference of the MRI with the sensitive measurement of electrical scalp potentials. Post-processing algorithms based on average artefact subtraction (AAS) have proven to be efficient in removing the MGA. However, the residual MGA after AAS still limits the quality and usable bandwidth of the EEG data despite further reduction through re-sampling, principal component analysis (PCA), and regressive filtering. We recently demonstrated that the residual MGA can largely be avoided by means of hardware synchronization. Here we present a new software synchronization method, which substitutes hardware synchronization and facilitates the removal of motion artefacts by PCA. The effectiveness of the retrospective synchronization algorithm (Resync) is demonstrated by comparison to the aforementioned techniques. For this purpose, we also developed a method for simulating the MGA and we propose new concepts for quantifying and comparing the performance of post-processing algorithms for EEG-MRI data. Results indicate that the benefits of (retrospective) synchronization and PCA depend largely on the relative contribution of timing errors and motion artefacts to the residual MGA as well as the frequency range of interest.

Validation of ICA-based myogenic artifact correction for scalp and source-localized EEG

Muscle electrical activity, or "electromyogenic" (EMG) artifact, poses a serious threat to the validity of electroencephalography (EEG) investigations in the frequency domain. EMG is sensitive to a variety of psychological processes and can mask genuine effects or masquerade as legitimate neurogenic effects across the scalp in frequencies at least as low as the alpha band (8-13 Hz). Although several techniques for correcting myogenic activity have been described, most are subjected to only limited validation attempts. Attempts to gauge the impact of EMG correction on intracerebral source models (source "localization" analyses) are rarer still. Accordingly, we assessed the sensitivity and specificity of one prominent correction tool, independent component analysis (ICA), on the scalp and in the source-space using high-resolution EEG. Data were collected from 17 participants while neurogenic and myogenic activity was independently varied. Several protocols for classifying and discarding components classified as myogenic and non-myogenic artifact (e.g., ocular) were systematically assessed, leading to the exclusion of one-third to as much as three-quarters of the variance in the EEG. Some, but not all, of these protocols showed adequate performance on the scalp. Indeed, performance was superior to previously validated regression-based techniques. Nevertheless, ICA-based EMG correction exhibited low validity in the intracerebral source-space, likely owing to incomplete separation of neurogenic from myogenic sources. Taken with prior work, this indicates that EMG artifact can substantially distort estimates of intracerebral spectral activity. Neither regression- nor ICA-based EMG correction techniques provide complete safeguards against such distortions. In light of these results, several practical suggestions and recommendations are made for intelligently using ICA to minimize EMG and other common artifacts.

Physiological recordings: basic concepts and implementation during functional magnetic resonance imaging

Combining human functional neuroimaging with other forms of psychophysiological measurement, including autonomic monitoring, provides an empirical basis for understanding brain-body interactions. This approach can be applied to characterize unwanted physiological noise, examine the neural control and representation of bodily processes relevant to health and morbidity, and index covert expression of affective and cognitive processes to enhance the interpretation of task-evoked regional brain activity. In recent years, human neuroimaging has been dominated by functional magnetic resonance imaging (fMRI) studies. The spatiotemporal information of fMRI regarding central neural activity is valuably complemented by parallel physiological monitoring, yet such studies still remain in the minority. This review article highlights fMRI studies that employed cardiac, vascular, respiratory, electrodermal, gastrointestinal and pupillary psychophysiological indices to address specific questions regarding interaction between brain and bodily state in the context of experience, cognition, emotion and behaviour. Physiological monitoring within the fMRI environment presents specific technical issues, most importantly related to safety. Mechanical and electrical hazards may present dangers to scanned subjects, operator and/or equipment. Furthermore, physiological monitoring may interfere with the quality of neuroimaging data, or itself be compromised by artefacts induced by the operation of the scanner. We review the sources of these potential problems and the current approaches and advice to enable the combination of fMRI and physiological monitoring in a safe and effective manner.

Blind identification of evoked human brain activity with independent component analysis of optical data

Diffuse optical tomography (DOT) methods observe hemodynamics in the brain by measuring light transmission through the scalp, skull, and brain. Thus, separating signals due to heart pulsations, breathing movements, and systemic blood flow fluctuations from the desired brain functional responses is critical to the fidelity of the derived maps. Herein, we applied independent component analysis (ICA) to temporal signals obtained from a high-density DOT system used for functional mapping of the visual cortex. DOT measurements were taken over the occipital cortex of human adult subjects while they viewed stimuli designed to activate two spatially distinct areas of the visual cortex. ICA was able to extract clean functional hemodynamic signals and separate brain activity sources from hemodynamic fluctuations related to heart and breathing without knowledge of the stimulus paradigm. Furthermore, independent components were found defining distinct functional responses to each stimulus type. Images generated from single ICA components were comparable, with regard to spatial extent and resolution, to images from block averaging (with knowledge of the block stimulus paradigm). Both images and estimated time-series signals demonstrated that ICA was superior to principal component analysis in extracting the true event-evoked response signals. Our results suggest that ICA can extract the time courses and the corresponding spatial extent of functional responses in DOT imaging.

Development, validation, and comparison of ICA-based gradient artifact reduction algorithms for simultaneous EEG-spiral in/out and echo-planar fMRI recordings

EEG data acquired in an MRI scanner are heavily contaminated by gradient artifacts that can significantly compromise signal quality. We developed two new methods based on independent component analysis (ICA) for reducing gradient artifacts from spiral in-out and echo-planar pulse sequences at 3 T, and compared our algorithms with four other commonly used methods: average artifact subtraction (Allen, P., Josephs, O., Turner, R., 2000. A method for removing imaging artifact from continuous EEG recorded during functional MRI. NeuroImage 12, 230-239.), principal component analysis (Niazy, R., Beckmann, C., Iannetti, G., Brady, J., Smith, S., 2005. Removal of FMRI environment artifacts from EEG data using optimal basis sets. NeuroImage 28, 720-737.), Taylor series ( Wan, X., Iwata, K., Riera, J., Kitamura, M., Kawashima, R., 2006. Artifact reduction for simultaneous EEG/fMRI recording: adaptive FIR reduction of imaging artifacts. Clin. Neurophysiol. 117, 681-692.) and a conventional temporal ICA algorithm. Models of gradient artifacts were derived from simulations as well as a water phantom and performance of each method was evaluated on datasets constructed using visual event-related potentials (ERPs) as well as resting EEG. Our new methods recovered ERPs and resting EEG below the beta band (<12.5 Hz) with high signal-to-noise ratio (SNR>4). Our algorithms outperformed all of these methods on resting EEG in the theta and alpha bands (SNR>4); however, for all methods, signal recovery was modest (SNR approximately 1) in the beta band and poor (SNR<0.3) in the gamma band and above. We found that the conventional ICA algorithm performed poorly with uniformly low SNR (<0.1). Taken together, our new ICA-based methods offer a more robust technique for gradient artifact reduction when scanning at 3 T using spiral in-out and echo-planar pulse sequences. We provide new insights into the strengths and weaknesses of each method using a unified subspace framework.

Ultrahigh-frequency EEG during fMRI: pushing the limits of imaging-artifact correction

Although solutions for imaging-artifact correction in simultaneous EEG-fMRI are improving, residual artifacts after correction still considerably affect the EEG spectrum in the ultrafast frequency band above 100 Hz. Yet this band contains subtle but valuable physiological signatures such as fast gamma oscillations or evoked high-frequency (600 Hz) bursts related to spiking of thalamocortical and cortical neurons. Here we introduce a simultaneous EEG-fMRI approach that integrates hard and software modifications for continuous acquisition of ultrafast EEG oscillations during fMRI. Our approach is based upon and extends the established method of averaged artifact subtraction (AAS). Particularly for recovery of ultrahigh-frequency EEG signatures, AAS requires invariantly sampled and constant imaging-artifact waveforms to achieve optimal imaging-artifact correction. Consequently, we adjusted our acquisition setup such that both physiological ultrahigh-frequency EEG and invariantly sampled imaging artifacts were captured. In addition, we extended the AAS algorithm to cope with other, non-sampling related sources of imaging-artifact variations such as subject movements. A cascaded principal component analysis finally removed remaining imaging-artifact residuals. We provide a detailed evaluation of averaged ultrahigh-frequency signals and unaveraged broadband EEG spectra up to 1 kHz. Evoked nanovolt-sized high-frequency bursts were successfully recovered during periods of MR data acquisition afflicted by imaging artifacts in the millivolt range. Compared to periods without imaging artifacts they exhibited the same mean amplitudes, latencies and waveforms and a signal-to-noise ratio of 72%. Furthermore we identified consistent dipole sources. In conclusion, ultrafast EEG oscillations can be continuously monitored during fMRI using the proposed approach.

Rolandic alpha and beta EEG rhythms' strengths are inversely related to fMRI-BOLD signal in primary somatosensory and motor cortex

Similar to the posterior alpha rhythm, pericentral (Rolandic) EEG rhythms in the alpha and beta frequency range are referred to as "idle rhythms" indicating a "resting state" of the respective system. The precise function of these rhythms is not clear. We used simultaneous EEG-fMRI during a bimanual motor task to localize brain areas involved in Rolandic alpha and beta EEG rhythms. The identification of these rhythms in the MR environment was achieved by a blind source separation algorithm. Rhythm "strength", i.e. spectral power determined by wavelet analysis, inversely correlated most strongly with the fMRI-BOLD signal in the postcentral cortex for the Rolandic alpha (mu) rhythm and in the precentral cortex for the Rolandic beta rhythm. FMRI correlates of Rolandic alpha and beta rhythms were distinct from those associated with the posterior "classical" alpha rhythm, which correlated inversely with the BOLD signal in the occipital cortex. An inverse correlation with the BOLD signal in the respective sensory area seems to be a general feature of "idle rhythms".

Electromyogenic artifacts and electroencephalographic inferences

Muscle or electromyogenic (EMG) artifact poses a serious risk to inferential validity for any electroencephalography (EEG) investigation in the frequency-domain owing to its high amplitude, broad spectrum, and sensitivity to psychological processes of interest. Even weak EMG is detectable across the scalp in frequencies as low as the alpha band. Given these hazards, there is substantial interest in developing EMG correction tools. Unfortunately, most published techniques are subjected to only modest validation attempts, rendering their utility questionable. We review recent work by our laboratory quantitatively investigating the validity of two popular EMG correction techniques, one using the general linear model (GLM), the other using temporal independent component analysis (ICA). We show that intra-individual GLM-based methods represent a sensitive and specific tool for correcting on-going or induced, but not evoked (phase-locked) or source-localized, spectral changes. Preliminary work with ICA shows that it may not represent a panacea for EMG contamination, although further scrutiny is strongly warranted. We conclude by describing emerging methodological trends in this area that are likely to have substantial benefits for basic and applied EEG research.

Realignment parameter-informed artefact correction for simultaneous EEG-fMRI recordings

In this work we introduce a new algorithm to correct the imaging artefacts in the EEG signal measured during fMRI acquisition. The correction techniques proposed so far cannot optimally represent transitions, i.e. when abrupt changes of the artefact properties due to head movements occur. The algorithm developed here takes the head movement parameters from the fMRI signal into account to calculate adequate EEG artefact templates and subsequently correct the distorted EEG data. The data reported in this work demonstrate that the realignment parameter-informed algorithm outperforms the commonly used moving average algorithm if head movements occur. The superiority is reflected by comparing the residual variance after artefact correction with either method. The residual variance is lower around head-movements that exceed head deflections of about 1 mm when applying the realignment parameter-informed algorithm. Additionally, the signal to noise ratio of a surrogate event-related potential (ERP) increased by 10-40% for head displacements larger than 1 mm. The algorithm developed here is particularly suited for studies where head movements of the subject cannot be prevented as in studies with patients, children, or during sleep. Furthermore, the enhanced signal to noise ratio of a single trial ERP indicates the power of the presented algorithm for single trial ERP-fMRI studies in which EEG signal quality is a critical factor.

Rejection of pulse related artefact (PRA) from continuous electroencephalographic (EEG) time series recorded during functional magnetic resonance imaging (fMRI) using constraint independent component analysis (cICA)

Rejection of the pulse related artefact (PRA) from electroencephalographic (EEG) time series recorded simultaneously with fMRI data is difficult, particularly during NREM sleep because of the similarities between sleep slow waves and PRA, in both temporal and frequency domains and the need to work with non-averaged data. Here we introduce an algorithm based on constrained independent component analysis (cICA) for PRA removal. This method has several advantages: (1) automatic detection of the components corresponding to the PRA; (2) stability of the solution and (3) computational treatability. Using multichannel EEG recordings obtained in a 3 T MR scanner, with and without concomitant fMRI acquisition, we provide evidence for the sensitivity and specificity of the method in rejecting PRA in various sleep and waking conditions.

Ten Hertz thalamus stimulation increases tremor activity in the subthalamic nucleus in a patient with Parkinson's disease

OBJECTIVE

In patients with Parkinson's disease (PD) the effect of thalamic stimulation on tremor pathophysiology remains largely unclear. By recording local field potentials (LFPs) in the subthalamic nucleus (STN) while stimulating the nucleus ventralis intermedius thalami (VIM), information of the stimulation effects should be gained.

METHODS

We had the unique opportunity to intraoperatively record LFPs of the STN in a patient with PD while stimulating the VIM. VIM electrodes had been implanted 9 years previously because of tremor. Due to worsening of clinical symptoms an implantation of STN electrodes had become necessary.

RESULTS

High frequency stimulation in the VIM lowered the power of the tremor frequency band (4-7Hz) in the STN. In contrast, 10Hz VIM stimulation elevated the power of the tremor frequency band as well as STN-EMG coupling.

CONCLUSIONS

The effect of high frequency stimulation may explain the improvement of tremor in patients who are treated with VIM deep brain stimulation. The power elevation during 10Hz stimulation suggests that the pathological cerebral and cerebral-muscular communication in PD is mainly driven at 10Hz.

SIGNIFICANCE

The direct cerebral recordings support the view that a 10Hz network is a pathophysiological key mechanism in the generation of motor deficits in PD.

Model-based Bayesian filtering of cardiac contaminants from biomedical recordings

Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources of noise for other biomedical signals. In some recent works, a Bayesian filtering framework has been proposed for denoising the ECG signals. In this paper, it is shown that this framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such as the electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG. The proposed method is evaluated on simulated and real signals.

Adaptive filtering of ballistocardiogram artifact from EEG signals using the dilated discrete Hermite transform

Electroencephalogram (EEG) signals, when recorded within the strong magnetic field of an MRI scanner are subject to various artifacts, of which the ballistocardiogram (BCG) is one of the prominent ones affecting the quality of the EEG. The BCG artifact varies slightly in shape and amplitude for every cardiac cycle making it difficult to identify and remove. This paper proposes a novel method for the identification and elimination of this artifact using the shape basis functions of the new dilated discrete Hermite transform. In this study, EEG data within and outside the scanner was recorded. On removal of the BCG artifact for the EEG data recorded within the scanner, a significant reduction in amplitude at the frequencies associated with the BCG artifact was observed. In order to quantitatively assess the efficacy of this method, BCG artifact templates were added to segments of EEG signals recorded outside the scanner. These signals, when filtered using the proposed method, had no significant difference (p0.05) from the original signals, indicating that the technique satisfactorily eliminates the BCG artifact and does not introduce any distortions in the original signal. The method is computationally efficient for real-time implementation.