Fetal ECG (FECG) monitoring using abdominal maternal signals is a non-invasive technique that allows early detection of changes in fetal wellbeing. Several other signal components have stronger energy than the FECG, the most important being maternal ECG (MECG) and, especially during labor, uterine EMG. This study proposes a new method to subtract MECG after detecting and removing abdominal signal segments with high-amplitude variations due to uterine contractions. The method removes MECG from abdominal signals using an approximation of the current MECG segment based on a linear combination of previous MECG segments aligned on the R-peak. The coefficients of the linear model are computed so that the squared error of the approximation over the whole current segment is minimized. Abdominal signal segments strongly affected by uterine contractions are detected by applying median filtering. The methods proposed are tested on real abdominal data recorded during labor, with FECG recorded using scalp electrodes synchronously recorded for comparison.

Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios), based on five quantitative performance indices.

Several imaging techniques have identified different brain areas involved in the processing of noxious stimulation and thus in the constitution of pain. However, only little is known how these brain areas communicate with one another after activation by stimulus processing and which areas directionally affect or modulate the activity of succeeding areas. One measure for the analysis of such interactions is represented by the Granger Causality Index (GCI). In applying time-varying bivariate and partial variants of this concept (tvGCI), the aim of the present study was to investigate the interaction of neural activities between a set of scalp electrodes that best represent the brain electrical neural activity of major cortical areas involved in the processing of noxious laser-heat stimuli and their variation in time. Bivariate and partial tvGCIs were calculated within four different intervals of laser-evoked event-related potentials (LEPs) including a baseline period prior to stimulus application and three intervals immediately following stimulus application, i.e., between 170 and 200 ms (at the N2 component), between 260 and 320 ms (P2 component), and between 320 and 400 ms (P3 component of LEPs). Results show some similarities, but also some striking differences between bivariate and partial tvGCIs. These differences might be explained by the nature of bivariate and partial tvGCIs. However, both tvGCI approaches revealed a directed interaction between medial and lateral electrodes of the centroparietal region. This result was interpreted as a directed interaction between the anterior cingulate cortex and the secondary somatosensory cortex and the insula, structures that are significantly involved in the constitution of pain.

Motor imagery is the mental simulation of a motor act that includes preparation for movement, passive observations of action and mental operations of motor representations implicitly or explicitly. Motor imagery as preparation for immediate movement likely involves the motor executive brain regions. Implicit mental operations of motor representations are considered to underlie cognitive functions. Another problem concerning neuro-imaging studies on motor imagery is that the performance of imagination is very difficult to control. The ability of an individual to control its EEG may enable him to communicate without being able to control their voluntary muscles. Communication based on EEG signals does not require neuromuscular control and the individuals who have neuromuscular disorders and who may have no more control over any of their conventional communication abilities may still be able to communicate through a direct brain-computer interface. A brain-computer interface replaces the use of nerves and muscles and the movements they produce with electrophysiological signals and is coupled with the hardware and software that translate those signals into physical actions. One of the most important components of a brain-computer interface is the EEG feature extraction procedure. This paper presents an approach that uses self-organizing fuzzy neural network based time series prediction that performs EEG feature extraction in the time domain only. EEG is recorded from two electrodes placed on the scalp over the motor cortex. EEG signals from each electrode are predicted by a single fuzzy neural network. Features derived from the mean squared error of the predictions and from the mean squared of the predicted signals are extracted from EEG data by means of a sliding window. The architecture of the two auto-organizing fuzzy neural networks is a network with multi inputs and single output