Short-time fractal analysis of biological autoluminescence.

Biological systems manifest continuous weak autoluminescence, which is present even in the absence of external stimuli. Since this autoluminescence arises from internal metabolic and physiological processes, several works suggested that it could carry information in the time series of the detected photon counts. However, there is little experimental work which would show any difference of this signal from random Poisson noise and some works were prone to artifacts due to lacking or improper reference signals. Here we apply rigorous statistical methods and advanced reference signals to test the hypothesis whether time series of autoluminescence from germinating mung beans display any intrinsic correlations. Utilizing the fractional Brownian bridge that employs short samples of time series in the method kernel, we suggest that the detected autoluminescence signal from mung beans is not totally random, but it seems to involve a process with a negative memory. Our results contribute to the development of the rigorous methodology of signal analysis of photonic biosignals.

Suppression of overlearning in independent component analysis used for removal of muscular artifacts from electroencephalographic records.

This paper addresses the overlearning problem in the independent component analysis (ICA) used for the removal of muscular artifacts from electroencephalographic (EEG) records. We note that for short EEG records with high number of channels the ICA fails to separate artifact-free EEG and muscular artifacts, which has been previously attributed to the phenomenon called overlearning. We address this problem by projecting an EEG record into several subspaces with a lower dimension, and perform the ICA on each subspace separately. Due to a reduced dimension of the subspaces, the overlearning is suppressed, and muscular artifacts are better separated. Once the muscular artifacts are removed, the signals in the individual subspaces are combined to provide an artifact free EEG record. We show that for short signals and high number of EEG channels our approach outperforms the currently available ICA based algorithms for muscular artifact removal. The proposed technique can efficiently suppress ICA overlearning for short signal segments of high density EEG signals.

Regularization techniques in realistic Laplacian computation.

This paper explores regularization options for the ill-posed spline coefficient equations in the realistic Laplacian computation. We investigate the use of the Tikhonov regularization, truncated singular value decomposition, and the so-called lambda-correction with the regularization parameter chosen by the L-curve, generalized cross-validation, quasi-optimality, and the discrepancy principle criteria. The provided range of regularization techniques is much wider than in the previous works. The improvement of the realistic Laplacian is investigated by simulations on the three-shell spherical head model. The conclusion is that the best performance is provided by the combination of the Tikhonov regularization and the generalized cross-validation criterion-a combination that has never been suggested for this task before.

Poisson pre-processing of nonstationary photonic signals: Signals with equality between mean and variance.

Photonic signals are broadly exploited in communication and sensing and they typically exhibit Poisson-like statistics. In a common scenario where the intensity of the photonic signals is low and one needs to remove a nonstationary trend of the signals for any further analysis, one faces an obstacle: due to the dependence between the mean and variance typical for a Poisson-like process, information about the trend remains in the variance even after the trend has been subtracted, possibly yielding artifactual results in further analyses. Commonly available detrending or normalizing methods cannot cope with this issue. To alleviate this issue we developed a suitable pre-processing method for the signals that originate from a Poisson-like process. In this paper, a Poisson pre-processing method for nonstationary time series with Poisson distribution is developed and tested on computer-generated model data and experimental data of chemiluminescence from human neutrophils and mung seeds. The presented method transforms a nonstationary Poisson signal into a stationary signal with a Poisson distribution while preserving the type of photocount distribution and phase-space structure of the signal. The importance of the suggested pre-processing method is shown in Fano factor and Hurst exponent analysis of both computer-generated model signals and experimental photonic signals. It is demonstrated that our pre-processing method is superior to standard detrending-based methods whenever further signal analysis is sensitive to variance of the signal.

Differences in mean arterial pressure of young and elderly people measured by oscilometry during inflation and deflation of the arm cuff.

Systemic arterial blood pressure (BP) is one of the most important parameters of the cardiovascular system. An oscillometric NIBP monitor was specifically designed to measure oscillometric pulsations and mean arterial pressure (MAP) during inflation and deflation of the cuff. Nineteen healthy young (age 23.1±1.7 years; mean±SD) and 35 elderly (83.9±7.9 years; mean±SD) subjects were studied. Differential analysis of MAP during inflation and deflation show mean |ΔMAP|=2.9±2.6 mm Hg in the young group (mean±SD) and |ΔMAP|=6.3±5.2 mm Hg for seniors (mean±SD). There was a significant difference (p<0.05) in means of |ΔMAP| measured during cuff inflation and cuff deflation between both groups. In about 50% of elderly subjects |ΔMAP| was higher than 5 mm Hg. Potential clinical relevance of the method needs to be further evaluated.

Intra- and inter-hemispheric coupling of electroencephalographic 8-13 Hz rhythm in humans and force of static finger extension.

The coupling of electroencephalographic (EEG) 8-13 Hz oscillations during static right finger extension performed under four different force levels was analyzed in 12 right-handed subjects. Increases in force of static muscle contraction were accompanied by increases in the 8-13 Hz band coherence between the contralateral sensorimotor area (S1/M1) and the ipsilateral S1/M1, frontal and parietal cortex, between supplementary motor area and bilateral S1/M1, and between posterior parietal cortex and bilateral S1/M1. The results suggest increased functional coupling between primary and higher-order motor areas during increased motor effort.

Effects of muscle contraction on somatosensory event-related EEG power and coherence changes.

Effects of isometric muscle contraction on amplitude and coherence changes of EEG rhythms during repetitive cutaneous electrical stimulation were analyzed in 10 right-handed subjects. Subjects received electrical stimuli at intensity above pain threshold to their right middle finger while either squeezing a rubber tube with the right index finger and thumb, or keeping their ipsilateral hand muscles relaxed. EEG was recorded using 111 closely spaced electrodes. Somatosensory stimuli were followed by reduction of the relative 8-12 and 16-24 Hz band power (at 0.2-0.4 s) in bilateral primary sensorimotor cortices (S1/M1) and medial frontal cortex, and by a subsequent increase in 16-24 Hz band power (at 0.9 s). Isometric muscle contraction strongly suppressed these band power changes. The 8-12 and 16-24 Hz mean coherence in a wide region surrounding the contralateral S1/M1 and in the medial frontal cortex showed an initial decrease, partially paralleling band power changes, and later an increase. Ipsilateral S1/M1 showed a decrease in 8-12 Hz coupling only with the central and frontal electrodes of the same hemisphere. Muscle contraction reduced all coherence changes, but enhanced the 8-12 Hz coherence between ipsilateral S1/M1 and posterior parietal cortex. Early post-stimulus decrease of oscillatory coupling between S1/M1 and premotor cortex and between S1/M1 and medial frontal cortex suggests that these cortical regions act rather independently during processing of somatosensory information, and synchronize only later when the band power in contralateral S1/M1 increases. Motor cortex activation associated with ipsilateral hand muscle contraction interferes with cortical processing of somatosensory stimuli in S1/M1 cortices.

Potential approximation in realistic Laplacian computation.

OBJECTIVE: This paper aims to improve the shortcomings of the extant methodologies for realistic Laplacian (RL) computation, and correct the erroneous claims published in the past. METHODS: We implemented several variants of RL computation methods, using various potential approximation techniques and different regularization approaches. The individual variants of the RL computation were tested using simulations based on a realistic head model computed with the boundary element method (BEM). The results which disagreed with previously published works were further analyzed, and the reasons for the disagreement were identified. RESULTS: We identified the best regularization techniques for the surface potential approximation, and we showed that once these techniques are used there is often little difference between various potential approximations, which is in contrast with previous claims that promoted the radial basis function (RBF) approximation. Further, our analysis shows that the RBF approximation suffers from Runge phenomenon, which cannot be mitigated simultaneously for both deep and shallow sources; therefore, its good performance is guarantied only if a priori knowledge about the source depth is available. CONCLUSIONS: The previously published methodology for RL computation was not optimal. Improvements are possible if the newly suggested approach is used. SIGNIFICANCE: The methodology presented in our paper allows more efficient utilization of the RL, providing a useful tool for processing of high density EEG recordings. Presented techniques allow to achieve high EEG spatial resolution, and avoid unnecessary spatial blurring caused by the problems in the previously published RL methodology.

Statistical evaluation of coherence estimated from optimally beamformed signals.

In this paper we investigate a situation where we want to perform a coherence analysis of two signal sources, one of which is measured directly, and the other is measured through a sensor array affected by noise. To extract the latter signal, we suggest the use of the optimal beamforming with reference. We note, however, that this approach results in a coherence estimate that is noticeably biased, and cannot be evaluated by the known statistical tests. We therefore derive a new statistical test, that allows the evaluation of the biased coherence estimate. We illustrate the applicability of our methodology on the coherence analysis of EEG and EMG signals. We note that the suggested approach has several advantages over the surface Laplacian, which is a spatial filter commonly used in the EEG-EMG coherence analysis.

Electrode position scaling in realistic laplacian computation.

This note discusses the effects of the electrode position scaling on the realistic Laplacian (RL) computation. It is shown that when the RL is estimated with the help of Tikhonov regularization and the generalized cross-validation (GCV) criterion, improper electrode position scaling may influence the GCV criterion, which results in the decrease of RL precision. We identify what the proper scaling should be, and we provide a closer examination of how the GCV criterion is affected by the electrode position scaling.

High-resolution movement EEG classification.

The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the results of other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94-100%), but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related) EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem.