Hidden Markov model in nonnegative matrix factorization for fetal heart rate estimation using physiological priors.

Fetal heart rate (fHR) analysis remains the most common technique for detecting fetal distress when monitoring the fetal well-being during labor. If cardiotocography (CTG) is nowadays the non-invasive clinical reference technique for fHR measurement, it suffers from several drawbacks, hence an increasing interest towards alternative technologies, especially around abdominal ECG (aECG).An original solution, using a single abdominal lead, was recently proposed to address both the feasibility in clinical routine and the challenging detection of temporal events when facing interfered signals from real life conditions.

Comparison of Blind Source Separation Methods to Surface Electromyogram for Extensor Muscles of the Index and Little Fingers.

Crosstalk is the result of the propagation of muscle electrical signals on surface electromyogram channels simultaneously. The objective of this paper is to study the behavior of three blind source separation (BSS) methods for crosstalk reduction during finger extensor muscle contractions: FastICA, joint diagonalization of covariance matrices and optimal filtering. These methods have been tested on artificial mixtures defined by a temporal sum of the real signals from isolated contraction of two independent biomechanical muscles for the extension of the index and little finger.

Fetal ECG denoising using dynamic time warping template subtraction.

The analysis of the fetal electrocardiogram (ECG) requires to remove the mother ECG (mECG) from the abdominal ECG signals. Template subtraction is a method that consists in modeling and removing the mECG's mean period i.e.

Residual convolutional autoencoder combined with a non-negative matrix factorization to estimate fetal heart rate.

The fetal heart rate (fHR) plays an important role in the determination of the good health of the fetus. Beside the traditional Doppler ultrasound technique, non-invasive fetal electrocardiography (fECG) has become an interesting alternative. However, extracting clean fECG from abdominal ECG (aECG) recordings is a challenging task due to the presence of the maternal ECG component and various noise sources.

U-Net Neural Network for Heartbeat Detection in Ballistocardiography.

Monitoring vital signs of neonates can be harmful and lead to developmental troubles. Ballistocardiography, a contactless heart rate monitoring method, has the potential to reduce this monitoring pain. However, signal processing is uneasy due to noise, inherent physiological variability and artifacts (e.

Fetal heart rate estimation from a single phonocardiogram signal using non-negative matrix factorization.

Fetal heart rate (FHR) is an important characteristic in fetal well-being follow-up. It is classically estimated using the cardiotocogram (CTG) non-invasive reference technique. However, this latter presents some significant drawbacks.

Is there an Optimal Localization of Cardio-microphone Sensors for Phonocardiogram Analysis?

Heart auscultation is one of the most useful medical diagnostic tools for getting valuable information of heart valves and heart hemodynamics functions. However, the information acquired by a traditional stethoscope can be inaccurate and insufficient. Phonocardiogram (PCG) was developed to improve accuracy through visual inspection and analysis.

Temporal Dynamics of Natural Static Emotional Facial Expressions Decoding: A Study Using Event- and Eye Fixation-Related Potentials.

This study aims at examining the precise temporal dynamics of the emotional facial decoding as it unfolds in the brain, according to the emotions displayed. To characterize this processing as it occurs in ecological settings, we focused on unconstrained visual explorations of natural emotional faces (i.e.

Estimation of overlapped Eye Fixation Related Potentials: The General Linear Model, a more flexible framework than the ADJAR algorithm.

The Eye Fixation Related Potential (EFRP) estimation is the average of EEG signals across epochs at ocular fixation onset. Its main limitation is the overlapping issue. Inter Fixation Intervals (IFI) - typically around 300 ms in the case of unrestricted eye movement- depend on participants' oculomotor patterns, and can be shorter than the latency of the components of the evoked potential.

Regularization and a general linear model for event-related potential estimation.

The usual event-related potential (ERP) estimation is the average across epochs time-locked on stimuli of interest. These stimuli are repeated several times to improve the signal-to-noise ratio (SNR) and only one evoked potential is estimated inside the temporal window of interest. Consequently, the average estimation does not take into account other neural responses within the same epoch that are due to short inter stimuli intervals.

Feasibility of BCI Control in a Realistic Smart Home Environment.

Smart homes have been an active area of research, however despite considerable investment, they are not yet a reality for end-users. Moreover, there are still accessibility challenges for the elderly or the disabled, two of the main potential targets for home automation. In this exploratory study we design a control mechanism for smart homes based on Brain Computer Interfaces (BCI) and apply it in the "Domus" smart home platform in order to evaluate the potential interest of users about BCIs at home.

Multimodal approach to estimate the ocular movements during EEG recordings: A coupled tensor factorization method.

This paper deals with coupled tensor factorization. A relaxed criterion derived from the advanced coupled matrix-tensor factorization (ACMTF) proposed by Acar et al. is described.

An application of Gaussian processes on ocular artifact removal from EEG.

Consequences of eye movements are one of the main interferences that distort the brain EEG recordings. In this paper, a multi-modal approach is used to estimate the ocular artifacts in the EEG: both vertical and horizontal eye movement signals recorded by an eye tracker are used as a reference to denoise the EEG. A Gaussian process, i.

Modeling quasi-periodic signals by a non-parametric model: application on fetal ECG extraction.

Quasi-periodic signals can be modeled by their second order statistics as Gaussian process. This work presents a non-parametric method to model such signals. ECG, as a quasi-periodic signal, can also be modeled by such method which can help to extract the fetal ECG from the maternal ECG signal, using a single source abdominal channel.

Correction: cecotti, h. And rivet, B. Subject combination and electrode selection in cooperative brain-computer interface based on event related potentials. Brain sci. 2014, 4, 335-355.

The authors wish to make the following correction to this paper (Cecotti, H.; Rivet, B. Subject Combination and Electrode Selection in Cooperative Brain-Computer Interface Based on Event Related Potentials.

Subject combination and electrode selection in cooperative brain-computer interface based on event related potentials.

New paradigms are required in Brain-Computer Interface (BCI) systems for the needs and expectations of healthy people. To solve this issue, we explore the emerging field of cooperative BCIs, which involves several users in a single BCI system. Contrary to classical BCIs that are dependent on the unique subject's will, cooperative BCIs are used for problem solving tasks where several people shall be engaged by sharing a common goal.

Fetal ECG extraction by extended state Kalman filtering based on single-channel recordings.

In this paper, we present an extended nonlinear Bayesian filtering framework for extracting electrocardiograms (ECGs) from a single channel as encountered in the fetal ECG extraction from abdominal sensor. The recorded signals are modeled as the summation of several ECGs. Each of them is described by a nonlinear dynamic model, previously presented for the generation of a highly realistic synthetic ECG.

Adaptive training session for a P300 speller brain-computer interface.

With a brain-computer interface (BCI), it is nowadays possible to achieve a direct pathway between the brain and computers thanks to the analysis of some particular brain activities. The detection of even-related potentials, like the P300 in the oddball paradigm exploited in P300-speller, provides a way to create BCIs by assigning several detected ERP to a command. Due to the noise present in the electroencephalographic signal, the detection of an ERP and its different components requires efficient signal processing and machine learning techniques.

EEG sensor selection by sparse spatial filtering in P300 speller brain-computer interface.

A Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by direct control from decoding of brain activity. This paper deals with the P300-speller application that enables to write a text based on the oddball paradigm. To improve the ergonomics and minimize the cost of such a BCI, reducing the number of electrodes is mandatory.

A study of lip movements during spontaneous dialog and its application to voice activity detection.

This paper presents a quantitative and comprehensive study of the lip movements of a given speaker in different speech/nonspeech contexts, with a particular focus on silences (i.e., when no sound is produced by the speaker).

xDAWN algorithm to enhance evoked potentials: application to brain-computer interface.

A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin . An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected into the estimated signal subspace.

Subspace estimation approach to P300 detection and application to brain-computer interface.

Brain-computer interface (BCI) is a system for direct communication between brain and computer. In this work, a new unsupervised algorithm is introduced for P300 subspace estimation: the raw EEG are thus enhanced by projection on the estimated subspace. Moreover a simple scheme to detect the P300 potentials in the human EEG by dimension reduction and linear support vector machine (SVM) is proposed to build a BCI based on the P300 speller.

Topographical dynamics of brain connections for the design of asynchronous brain-computer interfaces.

This article presents a new processing method to design brain-computer interfaces (BCIs). It shows how to use the perturbations of the communication between different cortical areas due to a cognitive task. For this, the network of the cerebral connections is built from correlations between cortical areas at specific frequencies and is analyzed using graph theory.