Ballistocardiogram Research Articles

Adaptive Cancellation of Floor Vibrations in Standing Ballistocardiogram Measurements Using a Seismic Sensor as a Noise Reference

An adaptive noise canceller was used to reduce the effect of floor vibrations on ballistocardiogram (BCG) measurements from a modified electronic bathroom scale. A seismic sensor was placed next to the scale on the floor and used as the noise reference input to the noise canceller. BCG recordings were acquired from a healthy subject while another person stomped around the scale, thus causing increased floor vibrations. The noise canceller substantially eliminated the artifacts in the BCG signal due to these vibrations without distorting the morphology of the measured BCG. Additionally, recordings were obtained from another subject standing inside a parked bus while the engine was running. The artifacts due to the vibrations of the engine, and the other vehicles moving on the road next to the bus, were also effectively eliminated by the noise canceller. The system with automatic floor vibration cancellation could be used to increase BCG measurement robustness in home monitoring applications. Additionally, the noise cancellation approach may enable BCG recording in ambulances-or other transport vehicles-where noninvasive hemodynamic monitoring may otherwise not be feasible.

Attenuation of artifacts in EEG signals measured inside an MRI scanner using constrained independent component analysis

Integration of electroencephalography (EEG) and functional magnetic imaging (fMRI) resonance will allow analysis of the brain activities at superior temporal and spatial resolution. However simultaneous acquisition of EEG and fMRI is hindered by the enhancement of artifacts in EEG, the most prominent of which are ballistocardiogram (BCG) and electro-oculogram (EOG) artifacts. The situation gets even worse if the evoked potentials are measured inside MRI for their minute responses in comparison to the spontaneous brain responses. In this study, we propose a new method of attenuating these artifacts from the spontaneous and evoked EEG data acquired inside an MRI scanner using constrained independent component analysis with a priori information about the artifacts as constraints. With the proposed techniques of reference function generation for the BCG and EOG artifacts as constraints, our new approach performs significantly better than the averaged artifact subtraction (AAS) method. The proposed method could be an alternative to the conventional ICA method for artifact attenuation, with some advantages. As a performance measure we have achieved much improved normalized power spectrum ratios (INPS) for continuous EEG and correlation coefficient (cc) values with outside MRI visual evoked potentials for visual evoked EEG, as compared to those obtained with the AAS method. The results show that our new approach is more effective than the conventional methods, almost fully automatic, and no extra ECG signal measurements are involved.

Non-invasive cardiac output trending during exercise recovery on a bathroom-scale-based ballistocardiograph

Cardiac ejection of blood into the aorta generates a reaction force on the body that can be measured externally via the ballistocardiogram (BCG). In this study, a commercial bathroom scale was modified to measure the BCGs of nine healthy subjects recovering from treadmill exercise. During the recovery, Doppler echocardiogram signals were obtained simultaneously from the left ventricular outflow tract of the heart. The percentage changes in root-mean-square (RMS) power of the BCG were strongly correlated with the percentage changes in cardiac output measured by Doppler echocardiography (R2 = 0.85, n = 275 data points). The correlation coefficients for individually analyzed data ranged from 0.79 to 0.96. Using Bland–Altman methods for assessing agreement, the mean bias was found to be −0.5% (±24%) in estimating the percentage changes in cardiac output. In contrast to other non-invasive methods for trending cardiac output, the unobtrusive procedure presented here uses inexpensive equipment and could be performed without the aid of a medical professional.

Removal of BCG Artifacts Using a Non-Kirchhoffian Overcomplete Representation

We present a nonlinear unmixing approach for extracting the ballistocardiogram (BCG) from EEG recorded in an MR scanner during simultaneous acquisition of functional MRI (fMRI). First, an overcomplete basis is identified in the EEG based on a custom multipath EEG electrode cap. Next, the overcomplete basis is used to infer non-Kirchhoffian latent variables that are not consistent with a conservative electric field. Neural activity is strictly Kirchhoffian while the BCG artifact is not, and the representation can hence be used to remove the artifacts from the data in a way that does not attenuate the neural signals needed for optimal single-trial classification performance. We compare our method to more standard methods for BCG removal, namely independent component analysis and optimal basis sets, by looking at single-trial classification performance for an auditory oddball experiment. We show that our overcomplete representation method for removing BCG artifacts results in better single-trial classification performance compared to the conventional approaches, indicating that the derived neural activity in this representation retains the complex information in the trial-to-trial variability.

Robust ballistocardiogram acquisition for home monitoring

The ballistocardiogram (BCG) measures the reaction of the body to cardiac ejection forces, and is an effective, non-invasive means of evaluating cardiovascular function. A simple, robust method is presented for acquiring high-quality, repeatable BCG signals from a modified, commercially available scale. The measured BCG waveforms for all subjects qualitatively matched values in the existing literature and physiologic expectations in terms of timing and IJ amplitude. Additionally, the BCG IJ amplitude was shown to be correlated with diastolic filling time for a subject with premature atrial contractions, demonstrating the sensitivity of the apparatus to beat-by-beat hemodynamic changes. The signal-to-noise ratio (SNR) of the BCG was estimated using two methods, and the average SNR over all subjects was greater than 12 for both estimates. The BCG measurement was shown to be repeatable over 50 recordings taken from the same subject over a three week period. This approach could allow patients at home to monitor trends in cardiovascular health.

Ballistocardiogram Artifact Removal in EEG-fMRI Signals Using Discrete Hermite Transforms

Simultaneously recorded electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) is rapidly emerging as a powerful neurophysiological research and clinical tool. However, the quality of the EEG, recorded in the MRI scanner, is affected by the ballistocardiogram (BCG), which is an artifact related to the cardiac cycle. The BCG has a complete spectral overlap with the EEG and is nonstationary over time, making its suppression a signal processing challenge. We propose a novel method for the identification and suppression of this artifact using shape basis functions of the new dilated discrete Hermite transform. The BCG artifacts are modeled continuously, using these discrete Hermite basis functions and are subsequently subtracted from the ongoing EEG. Experimental EEG data was recorded within and outside a 3 Tesla MRI scanner, from a total of 6 subjects under a variety of experimental conditions. The efficiency of this algorithm was quantitatively assessed by adding known BCG templates, at varying Signal to Noise Ratios (SNRs), to EEG recorded outside the scanner. Significant suppression of the BCG artifact (p<0.05) was achieved without distorting the underlying EEG. Using EEG data recorded inside the MR scanner, this method was compared with existing BCG artifact removal techniques and its performance was found to be superior to the Average Artifact Subtraction (AAS) method and comparable to the Independent Component Analysis (ICA) based methods. The computational simplicity of this technique allows for real time implementation.

An Electromechanical Film Sensor Based Wireless Ballistocardiographic Chair: Implementation and Performance

New sensor technologies open possibilities for measuring traditional biosignals in new innovative ways. This, together with the development of signal processing systems and their increasing computing power, can sometimes give new life to old measurement techniques. Ballistocardiogram (BCG) is one such technique, originally promising but later replaced by the now very popular electrocardiogram. It's usability was previously limited by the large size of the devices required to record it, and the complex nature of the recorded signal, which gave little information in visual inspection. In this paper, we present how a lightweight and flexible electromechanical film (EMFi) sensor can be used to record BCG. A ballistocardiographic chair, designed to look like a normal office chair, was built and fitted with two sensitive EMFi sensors. Two different measurement setups to record the signal from the EMFi sensors were developed. The first, so-called wired setup, uses a commercial bio-amplifier, and a special pre-amplifier to interface to it. The latter, so-called wireless setup, uses our own hardware to transmit the recorded digitized signals wirelessly to a nearby PC. Both of these systems are presented and their performance evaluated. Also, the suitability, limitations and advantages of the EMFi sensor over existing sensors and methods are discussed. The validity of the EMFi sensor and amplifier output is tested using a mechanical vibrator. Lastly, a summary of signal analysis methods developed for our system is given. The developed systems have be used for medical BCG measurements, and the recordings indicate that the both the systems are functional and capture useful BCG signal components.

Improvement of ballistocardiogram processing by inclusion of respiration information

In this paper a novel methodology for processing of a ballistocardiogram (BCG) is proposed in which the respiration signal is utilized to improve the averaging of the BCG signal and ultimately the annotation and interpretation of the signal. Previous research works filtered out the respiration signal while the novelty of the current research is that, rather than removing the respiration effect from the signal, we utilize the respiration information to improve the averaging and thus analysis and interpretation of the BCG signal in diagnosis of cardiac malfunctions. This methodology is based on our investigation that BCG cycles corresponding to the inspiration and expiration phases of the respiration cycle are different in morphology. BCG cycles corresponding to the expiration phase of respiration have been proved to be more closely related to each other when compared to cycles corresponding to inspiration, and therefore expiration cycles are better candidates to be selected for the calculation of the averaged BCG signal. The new BCG average calculated based on this methodology is then considered as the representative and a template of the BCG signal for further processing. This template can be considered as the output of a clinical BCG instrument with higher reliability and accuracy compared to the previous processing methods.

Artifact reduction performance of different algorithms for simultaneous recordings of EEG and fMRI

Event Abstract Back to Event Artifact reduction performance of different algorithms for simultaneous recordings of EEG and fMRI Basri Erdogan1*, Zübeyir Bayraktaroglu2, A. Bayram3, B. Bilgiç3 and T. Ölmez1 1 Istanbul Technical University , Turkey 2 Istanbul University, Turkey 3 NPI Hospital, Turkey Purpose: EEG data recorded during fMRI is heavily contaminated by imaging and ballistocardiogram (BCG) artifacts. Imaging artifact is caused by switching of the MR gradients and BCG is mostly caused by movements of the EEG electrodes due to cardiac activity. Here we evaluate the performances of several algorithms to remove both imaging and BCG artifacts in EEG. Methods: EEG was recorded inside a 1.5 T scanner with a 32 channel MR-compatible BrainAmp system. Artifact reduction is performed on the steady state visual evoked potential (VEP) recordings. The reason why we chose this paradigm is that steady state VEP forms clearly visible frequency peaks in the EEG spectrum, which makes it easier to differentiate the artifact and the EEG data and to evaluate the performances of the artifact reduction algorithms. To perform imaging artifact reduction we chose the two least complex and also computationally efficient methods, which are Image Artifact Reduction (IAR) with marker alignment and Frequency Domain Filtering (FDF). For BCG reduction, the two most popular methods are used, which are Average Artifact Subtraction (AAS) and Independent Component Analysis (ICA). For ICA, EEGLAB toolbox is used that is based on the Infomax algorithm and artifact related independent components are detected by visual inspection. Results and Discussion: IAR and FDF methods both successfully eliminated the imaging artifact on visual evoked potentials. Frequency Domain Filtering method needs a base signal recorded in MR without fMRI recording. But it does not use MR markers, which requires extra hardware to be recorded. After IAR method applied, some high frequency residual artifacts remained. However, they were easily eliminated by low-pass filtering, since the sub 40 Hz range was not contaminated by this type of artifact. There was no residual artifact problem with FDF method, but it suffered from ringing artifacts occurring at the boundaries of the signals, which is not a major problem in the case of continuous fMRI recording. However, it may become important for interleaved recordings. In the BCG case, different groups used ICA and AAS in their studies to reduce BCG artifacts and reported varying results on the performances of ICA and AAS. In our study ICA and AAS gave comparable results. ICA provided better BCG suppression in general. Nevertheless, it also suppressed some VEP components at specific frequencies. We found AAS more reliable, because ICA seemed to be prone to deteriorate the frequencies that are not related with BCG. It also deteriorated the topography of the steady-state VEP responses at certain stimulation frequencies. Conference: 10th International Conference on Cognitive Neuroscience, Bodrum, Turkey, 1 Sep - 5 Sep, 2008. Presentation Type: Poster Presentation Topic: Neuroinformatics of Cognition Citation: Erdogan B, Bayraktaroglu Z, Bayram A, Bilgiç B and Ölmez T (2008). Artifact reduction performance of different algorithms for simultaneous recordings of EEG and fMRI. Conference Abstract: 10th International Conference on Cognitive Neuroscience. doi: 10.3389/conf.neuro.09.2009.01.348 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 15 Dec 2008; Published Online: 15 Dec 2008. * Correspondence: Basri Erdogan, Istanbul Technical University, Istanbul, Turkey, basri_erd@yahoo.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Basri Erdogan Zübeyir Bayraktaroglu A. Bayram B. Bilgiç T. Ölmez Google Basri Erdogan Zübeyir Bayraktaroglu A. Bayram B. Bilgiç T. Ölmez Google Scholar Basri Erdogan Zübeyir Bayraktaroglu A. Bayram B. Bilgiç T. Ölmez PubMed Basri Erdogan Zübeyir Bayraktaroglu A. Bayram B. Bilgiç T. Ölmez Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

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.

Spatial filters and automated spike detection based on brain topographies improve sensitivity of EEG–fMRI studies in focal epilepsy

The ballistocardiogram (BCG) represents one of the most prominent sources of artifacts that contaminate the electroencephalogram (EEG) during functional MRI. The BCG artifacts may affect the detection of interictal epileptiform discharges (IED) in patients with epilepsy, reducing the sensitivity of the combined EEG–fMRI method. In this study we improved the BCG artifact correction using a multiple source correction (MSC) approach. On the one hand, a source analysis of the IEDs was applied to the EEG data obtained outside the MRI scanner to prevent the distortion of EEG signals of interest during the correction of BCG artifacts. On the other hand, the topographies of the BCG artifacts were defined based on the EEG recorded inside the scanner. The topographies of the BCG artifacts were then added to the surrogate model of IED sources and a combined source model was applied to the data obtained inside the scanner. The artifact signal was then subtracted without considerable distortion of the IED topography. The MSC approach was compared with the traditional averaged artifact subtraction (AAS) method. Both methods reduced the spectral power of BCG-related harmonics and enabled better detection of IEDs. Compared with the conventional AAS method, the MSC approach increased the sensitivity of IED detection because the IED signal was less attenuated when subtracting the BCG artifacts. The proposed MSC method is particularly useful in situations in which the BCG artifact is spatially correlated and time-locked with the EEG signal produced by the focal brain activity of interest.

Measurement and reduction of motion and ballistocardiogram artefacts from simultaneous EEG and fMRI recordings

Recording the electroencephalogram (EEG) during functional magnetic resonance imaging (fMRI) permits the identification of haemodynamic changes associated with EEG events. However, subject motion within the MR scanner can cause unpredictable and frustrating artefacts on the EEG that may appear focally, bilaterally or unilaterally and can sometimes be confused for epileptiform activity. Motion may arise from a number of sources: small involuntary cardiac-related body movements (ballistocardiogram); acoustic vibrations due to the scanner machinery; and voluntary subject movements. Here we describe a new real-time technique for removing ballistocardiogram (BCG) and movement artefact from EEG recordings in the MR scanner using a novel method for recording subject motion. We record the current induced in a number of wire loops, attached to a cap worn by the subject, due to motion in the static magnetic field of the scanner (Faraday's Law). This is the same process that leads to the motion artefacts on the EEG, and hence these signals are ideally suited to filtering these artefacts from the EEG. Our filter uses a linear adaptive technique based upon the Recursive Least Squares (RLS) algorithm. We demonstrate in both simulations and real EEG recordings from epilepsy patients that our filter significantly reduces the artefact power whilst preserving the underlying EEG signal.

An Intelligent Ballistocardiographic Chair using a Novel SF-ART Neural Network and Biorthogonal Wavelets

This paper presents a comparative analysis of novel supervised fuzzy adaptive resonance theory (SF-ART), multilayer perceptron (MLP) and Multi Layer Perceptrons (MLP) neural networks over Ballistocardiogram (BCG) signal recognition. To extract essential features of the BCG signal, we applied Biorthogonal wavelets. SF-ART performs classification on two levels. At first level, pre-classifier which is self-organized fuzzy ART tuned for fast learning classifies the input data roughly to arbitrary (M) classes. At the second level, post-classification level, a special array called Affine Look-up Table (ALT) with M elements stores the labels of corresponding input samples in the address equal to the index of fuzzy ART winner. However, in running (testing) mode, the content of an ALT cell with address equal to the index of fuzzy ART winner output will be read. The read value declares the final class that input data belongs to. In this paper, we used two well-known patterns (IRIS and Vowel data) and a medical application (Ballistocardiogram data) to evaluate and check SF-ART stability, reliability, learning speed and computational load. Initial tests with BCG from six subjects (both healthy and unhealthy people) indicate that the SF-ART is capable to perform with a high classification performance, high learning speed (elapsed time for learning around half second), and very low computational load compared to the well-known neural networks such as MLP which needs minutes to learn the training material. Moreover, to extract essential features of the BCG signal, we applied Biorthogonal wavelets. The applied wavelet transform requires no prior knowledge of the statistical distribution of data samples.

Applying Novel Time-Frequency Moments Singular Value Decomposition Method and Artificial Neural Networks for Ballistocardiography

As we know, singular value decomposition (SVD) is designed for computing singular values (SVs) of a matrix. Then, if it is used for finding SVs of an m-by-1 or 1-by-m array with elements representing samples of a signal, it will return only one singular value that is not enough to express the whole signal. To overcome this problem, we designed a new kind of the feature extraction method which we call time-frequency moments singular value decomposition (TFM-SVD). In this new method, we use statistical features of time series as well as frequency series (Fourier transform of the signal). This information is then extracted into a certain matrix with a fixed structure and the SVs of that matrix are sought. This transform can be used as a preprocessing stage in pattern clustering methods. The results in using it indicate that the performance of a combined system including this transform and classifiers is comparable with the performance of using other feature extraction methods such as wavelet transforms. To evaluate TFM-SVD, we applied this new method and artificial neural networks (ANNs) for ballistocardiogram (BCG) data clustering to look for probable heart disease of six test subjects. BCG from the test subjects was recorded using a chair-like ballistocardiograph, developed in our project. This kind of device combined with automated recording and analysis would be suitable for use in many places, such as home, office, and so forth. The results show that the method has high performance and it is almost insensitive to BCG waveform latency or nonlinear disturbance.

Improved quality of auditory event-related potentials recorded simultaneously with 3-T fMRI: Removal of the ballistocardiogram artefact

EEG signals recorded simultaneously with fMRI are massively compromised by severe artefacts, among them the cardiac cycle-related ballistocardiogram (BCG) artefact. Different methods have been proposed to remove the BCG artefact focusing on channel-wise template subtraction procedures or spatial filtering approaches such as independent component analysis (ICA). Here we systematically compared the performance of the optimal basis set (OBS), a channel-wise correction approach, with ICA and a recently proposed combination of both (OBS–ICA). The three different procedures were applied to 60-channel EEG data from 12 subjects recorded during fMRI acquisition in a 3-T scanner. In addition to examination of the residual BCG artefact, the signal-to-noise ratio (SNR) and the topography of the resulting auditory evoked potential component N1 were compared. Whereas all three approaches led to a significant artefact reduction, the ICA procedure resulted in a significantly reduced N1 SNR and amplitude when compared to BCG-uncorrected data, indicating a rather poor performance. In contrast to ICA, OBS and OBS–ICA corrected data substantially improved the SNR of the N1. The quality of the auditory evoked potential N1 topography was investigated by means of equivalent current dipole modelling. On a descriptive level, all three correction procedures led to a reduced localization error when compared to BCG-uncorrected data. This improvement was significant for OBS–ICA. We conclude that OBS and OBS–ICA can efficiently remove BCG artefacts and substantially improve the quality of EEG signals recorded inside the scanner, a prerequisite for the successful integration of simultaneously recorded EEG and fMRI.

Ballistocardiogram artifact removal from EEG signals using adaptive filtering of EOG signals

We estimated ballistocardiogram (BCG) components in EEG signals recorded inside an MRI magnet using the electro-oculogram (EOG) signals recorded simultaneously with the EEG signals. Since the EOG signals are measured near the EEG measuring points, it is thought that the BCG components in the EOG signals resemble the BCG components in the EEG signals. To estimate the BCG components in the EEG signals, we applied the Kalman filter to the EOG and EEG signals recorded inside a 3.0 T MRI magnet. After removing the estimated BCG components from the EEG signals, we extracted the visual-evoked potentials (VEPs) from the BCG-removed EEG signals. To validate the efficacy of Kalman filtering in the BCG artifact removal, we have compared three types of VEPs of eight healthy subjects: one extracted from the raw EEG signals measured outside the magnet and the others extracted from the BCG-removed EEG signals measured inside the magnet. The BCG artifacts have been removed with Kalman filtering as well as with the conventional BCG template subtraction method for the sake of comparison. No significant difference in waveforms, latencies and amplitudes has been found between the two types of VEPs extracted from the two kinds of BCG-removed EEG signals.

Removal of Ballistocardiogram Artifacts From Simultaneously Recorded EEG and fMRI Data Using Independent Component Analysis

Simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been studied to identify areas related to EEG events. EEG data recorded in the magnetic resonance (MR) scanner with MR imaging is suffered from two specific artifacts, imaging artifact, and ballistocardiogram (BCG). In this paper, we focus on BCG. In preceding studies, average subtraction was often used for this purpose. However, average subtraction requires an assumption that BCG waveforms are precisely periodic, which seems unrealistic because BCG is a biomedical artifact. We propose the application of independent component analysis (ICA) with a postprocessing of high-pass filtering for the removal of BCG. With this approach, it is not necessary to assume that the BCG waveform is periodic. Empirically, we show that our proposed method removes BCG artifacts as well as does the average subtraction method. Power spectral density analysis of the two approaches shows that, with ICA, distortion of recovered EEG data is also as small as that associated with the average subtraction approach. We also propose a hypothesis for how head movement causes BCGs and show why ICA can remove BCG artifacts arising from this source.

An independent component analysis-based approach on ballistocardiogram artifact removing

Interest about simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data acquisition has rapidly increased during the last years because of the possibility that the combined method offers to join temporal and spatial resolution, providing in this way a powerful tool to investigate spontaneous and evoked brain activities. However, several intrinsic features of MRI scanning become sources of artifacts on EEG data. Noise sources of a highly predictable nature such as those related to the pulse MRI sequence and those determined by magnetic gradient switching during scanning do not represent a major problem and can be easily removed. On the contrary, the ballistocardiogram (BCG) artifact, a large signal visible on all EEG traces and related to cardiac activity inside the magnetic field, is determined by sources that are not fully stereotyped and causing important limitations in the use of artifact-removing strategies. Recently, it has been proposed to use independent component analysis (ICA) to remove BCG artifact from EEG signals. ICA is a statistical algorithm that allows blind separation of statistically independent sources when the only available information is represented by their linear combination. An important drawback with most ICA algorithms is that they exhibit a stochastic behavior: each run yields slightly different results such that the reliability of the estimated sources is difficult to assess. In this preliminary report, we present a method based on running the FastICA algorithm many times with slightly different initial conditions. Clustering structure in the signal space of the obtained components provides us with a new way to assess the reliability of the estimated sources.

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.

Development of heart rate in the precocial king quail Coturnix chinensis.

Our aim was to examine changes in heart rate (fh) during the embryonic and posthatching periods of the smallest precocial avian species, Coturnix chinensis. In experiment I, repeated measurements of mean fh were made for individual quail by ballistocardiogram (BCG) during incubation, and by both piezo-electric film and electrocardiogram (ECG) during the posthatching period (resting and thermoneutral conditions). Mean fh of all embryos increased during the second half of incubation and the first week posthatching, but a few embryos experienced a very brief period of decreased fh prior to internal pipping. After the first week, fh of posthatching quail was maintained at high levels (550-650 beats min-1), then decreased with age and increase in body mass. The maximal fh of quail chicks represents a greater posthatching increase in fh than is found in larger precocial chickens, this difference being attributable to the higher demands of thermoregulation at small body masses in the quail. In experiment II, the mean fh of quail embryos (day 2-16) was recorded by ECG, and embryonic stage, yolk-free embryo mass (wet and dry) and water content were measured. Mean fh was linearly related to embryo mass throughout incubation, except on the day prior to internal pipping, when the fh of a few embryos declined below this linear relationship. Measurements of instantaneous fh of late incubation embryos, young and adult quail all showed spontaneous fluctuations in fh. Two main frequency components of fh fluctuations were identified for the first time in an avian species. Low-frequency (mean 0.09 Hz, 12.6 s) and high-frequency (1.4 Hz, 0.9 s) oscillations in both young chicks and adult quail were detected and are considered to reflect baroreflex mediation of fh and respiratory sinus arrhythmia, respectively.