Smartphone Voice Calls Provide Early Biomarkers of Parkinsonism in Rapid Eye Movement Sleep Behavior Disorder.

Background: Speech dysfunction represents one of the initial motor manifestations to develop in Parkinson's disease (PD) and is measurable through smartphone.

Objective: The aim was to develop a fully automated and noise-resistant smartphone-based system that can unobtrusively screen for prodromal parkinsonian speech disorder in subjects with isolated rapid eye movement sleep behavior disorder (iRBD) in a real-world scenario.

Methods: This cross-sectional study assessed regular, everyday voice call data from individuals with iRBD compared to early PD patients and healthy controls via a developed smartphone application.

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.

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.

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.

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.

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.

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).

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.

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.

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.

Comparison of different methods of time shift measurement in EEG.

Digital signal processing techniques are often used for measurement of small time shifts between EEG signals. In our work we tested properties of linear cross-correlation and phase/coherence method. The last mentioned method was used in two versions.

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.

Electroencephalographic correlates of paced breathing.

Multichannel EEG, respiration, blood pressure and ECG were recorded during paced breathing at five frequencies in 18 subjects in order to elucidate the effects of paced breathing on power changes in alpha, beta and theta bands, and on rhythmical variability of these parameters. Mean power in the beta band and low-frequency beta power variability (0.12-0.