GRU-powered sleep stage classification with permutation-based EEG channel selection.

We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan.

Newly identified Phonocardiography frequency bands for psychological stress detection with Deep Wavelet Scattering Network.

The timely psychological stress detection can improve the quality of human life by preventing stress-induced behavioral and pathological consequences. This paper presents a novel framework that eliminates the need of Electrocardiography (ECG) signals-based referencing of Phonocardiography (PCG) signals for psychological stress detection. This stand-alone PCG-based methodology uses wavelet scattering approach on the data acquired from twenty-eight healthy adult male and female subjects to detect psychological stress.

Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy.

Effective management of dementia requires the timely detection of mild cognitive impairment (MCI). This paper introduces a multi-objective optimization approach for selecting EEG channels (and features) for the purpose of detecting MCI. Firstly, each EEG signal from each channel is decomposed into subbands using either variational mode decomposition (VMD) or discrete wavelet transform (DWT).

EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels.

The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals.

Decoding Emotions From EEG Responses Elicited by Videos Using Machine Learning Techniques on Two Datasets.

In recent times, we have seen extensive research in the field of EEG-based emotion identification. The majority of solutions suggested by current literature use sophisticated deep learning techniques for the identification of human emotions. These models are very complex and need huge resources to implement.

Primary color decoding using deep learning on source reconstructed EEG signal responses.

The brain's response to visual stimuli of different colors might be used in a brain-computer interface (BCI) paradigm, for letting a user control their surroundings by looking at specific colors. Allowing the user to control certain elements in its environment, such as lighting and doors, by looking at corresponding signs of different colors could serve as an intuitive interface. This paper presents work on the development of an intra-subject classifier for red, green, and blue (RGB) visual evoked potentials (VEPs) in recordings performed with an electroencephalogram (EEG).

Mechanism of Reduction of Aqueous U(V)-dpaea and Solid-Phase U(VI)-dpaea Complexes: The Role of Multiheme -Type Cytochromes.

The biological reduction of soluble U(VI) complexes to form immobile U(IV) species has been proposed to remediate contaminated sites. It is well established that multiheme -type cytochromes (MHCs) are key mediators of electron transfer to aqueous phase U(VI) complexes for bacteria such as MR-1. Recent studies have confirmed that the reduction proceeds via a first electron transfer forming pentavalent U(V) species that readily disproportionate.

Emerging concepts in heart failure management and treatment: focus on point-of-care ultrasound in cardiogenic shock.

Point-of-care ultrasound (POCUS) plays a strategic role in the diagnostic and therapeutic evaluation of critically ill patients and, especially, in those who are haemodynamically unstable. In this context, POCUS allows a more precise identification of the cause, its differential diagnosis, the eventual coexistence with another entity and, finally, guiding of the therapeutic approach. It implies a portable use of ultrasound in acute settings covering different specified protocols, such as echocardiography, vascular, lung or abdominal ultrasound.

Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques.

Early detection of Parkinson's disease (PD) is very important in clinical diagnosis for preventing disease development. In this study, we present efficient discrete wavelet transform (DWT)-based methods for detecting PD from health control (HC) in two cases, namely, off-and on-medication. First, the EEG signals are preprocessed to remove major artifacts before being decomposed into several EEG sub-bands (approximate and details) using DWT.

Automated methodology for optimal selection of minimum electrode subsets for accurate EEG source estimation based on Genetic Algorithm optimization.

High-density Electroencephalography (HD-EEG) has proven to be the EEG montage that estimates the neural activity inside the brain with highest accuracy. Multiple studies have reported the effect of electrode number on source localization for specific sources and specific electrode configurations. The electrodes for these configurations are often manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, but electrode configurations are not often selected according to their contribution to estimation accuracy.

Two-dimensional CNN-based distinction of human emotions from EEG channels selected by multi-objective evolutionary algorithm.

In this study we explore how different levels of emotional intensity (Arousal) and pleasantness (Valence) are reflected in electroencephalographic (EEG) signals. We performed the experiments on EEG data of 32 subjects from the DEAP public dataset, where the subjects were stimulated using 60-s videos to elicitate different levels of Arousal/Valence and then self-reported the rating from 1 to 9 using the self-assessment Manikin (SAM). The EEG data was pre-processed and used as input to a convolutional neural network (CNN).

Biological Reduction of a U(V)-Organic Ligand Complex.

Metal-reducing microorganisms such as MR-1 reduce highly soluble species of hexavalent uranyl (U(VI)) to less mobile tetravalent uranium (U(IV)) compounds. The biologically mediated immobilization of U(VI) is being considered for the remediation of U contamination. However, the mechanistic underpinnings of biological U(VI) reduction remain unresolved.

A Power Efficient Low-noise and High Swing CMOS Amplifier for Neural Recording Applications.

In this paper, a power efficient, low-noise and high swing capacitively-coupled amplifier (CCA) for neural recording applications is proposed. The use of current splitting technique and current scaling technique in a current mirror operational transconductance amplifier (CM-OTA) has lead to a very good trade-off between power and noise. The presented architecture is simple, without cascode transistor while it has more than 80 dB open-loop gain without extra power consumption.

A Low-power and Low-noise Multi-purpose Chopper Amplifier with High CMRR and PSRR.

In this article, by choosing and optimizing suitable structure in each stage, we have designed a multi-purpose low noise chopper amplifier. The proposed neural chopper amplifier with high CMRR and PSRR is suitable for EEG, LFP and AP signals while it has a low NEF. In order to minimize the noise and increase the bandwidth, a single stage current reuse amplifier with pseudo-resistor common-mode feedback is chosen, while a simple fully differential amplifier is implemented at the second stage to provide high swing.

Towards a minimal EEG channel array for a biometric system using resting-state and a genetic algorithm for channel selection.

We present a new approach for a biometric system based on electroencephalographic (EEG) signals of resting-state, that can identify a subject and reject intruders with a minimal subset of EEG channels. To select features, we first use the discrete wavelet transform (DWT) or empirical mode decomposition (EMD) to decompose the EEG signals into a set of sub-bands, for which we compute the instantaneous and Teager energy and the Higuchi and Petrosian fractal dimensions for each sub-band. The obtained features are used as input for the local outlier factor (LOF) algorithm to create a model for each subject, with the aim of learning from it and rejecting instances not related to the subject in the model.

EEG Channel-Selection Method for Epileptic-Seizure Classification Based on Multi-Objective Optimization.

We present a multi-objective optimization method for electroencephalographic (EEG) channel selection based on the non-dominated sorting genetic algorithm (NSGA) for epileptic-seizure classification. We tested the method on EEG data of 24 patients from the CHB-MIT public dataset. The procedure starts by decomposing the EEG data from each channel into different frequency bands using the empirical mode decomposition (EMD) or the discrete wavelet transform (DWT), and then for each sub-band four features are extracted; two energy values and two fractal dimension values.

Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD.

We are here to present a new method for the classification of epileptic seizures from electroencephalogram (EEG) signals. It consists of applying empirical mode decomposition (EMD) to extract the most relevant intrinsic mode functions (IMFs) and subsequent computation of the Teager and instantaneous energy, Higuchi and Petrosian fractal dimension, and detrended fluctuation analysis (DFA) for each IMF. We validated the method using a public dataset of 24 subjects with EEG signals from 22 channels and showed that it is possible to classify the epileptic seizures, even with segments of six seconds and a smaller number of channels ( .

Oxidosqualene cyclases involved in the biosynthesis of triterpenoids in Quercus suber cork.

Cork is a water-impermeable, suberin-based material harboring lignin, (hemi)cellulose, and extractable small molecules (primarily triterpenoids). Extractables strongly influence the properties of suberin-based materials. Though these previous findings suggest a key role for triterpenoids in cork material quality, directly testing this idea is hindered in part because it is not known which genes control cork triterpenoid biosynthesis.

Multi-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification system.

We present a four-objective optimization method for optimal electroencephalographic (EEG) channel selection to provide access to subjects with permission in a system by detecting intruders and identifying the subject. Each instance was represented by four features computed from two sub-bands, extracted using empirical mode decomposition (EMD) for each channel, and the feature vectors were used as input for one-class/multi-class support vector machines (SVMs). We tested the method on data from the event-related potentials (ERPs) of 26 subjects and 56 channels.

Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition.

Several approaches can be used to estimate neural activity. The main differences between them concern the information used and its sensitivity to high noise levels. Empirical mode decomposition (EMD) has been recently applied to electroencephalography EEG-based neural activity reconstruction to provide time-frequency information to improve the estimation of neural activity.

Silencing against the conserved NAC domain of the potato StNAC103 reveals new NAC candidates to repress the suberin associated waxes in phellem.

Both suberin and its associated waxes contribute to the formation of apoplastic barriers that protect plants from the environment. Some transcription factors have emerged as regulators of the suberization process. The potato StNAC103 gene was reported as a repressor of suberin polyester and suberin-associated waxes deposition because its RNAi-mediated downregulation (StNAC103-RNAi) over-accumulated suberin and associated waxes in the tuber phellem concomitantly with the induction of representative biosynthetic genes.

Distributed control architecture for real-time model predictive control for system-level harmonic mitigation in power systems.

It can be challenging to design and implement Model Predictive Control (MPC) schemes in systems with fast dynamics. As MPCs often introduce high computational loads, it can be hard to assure real-time properties required by the dynamic system. An understanding of the system's behavior, to exploit system properties that can benefit real-time implementation is imperative.

Localization of Active Brain Sources From EEG Signals Using Empirical Mode Decomposition: A Comparative Study.

The localization of active brain sources from Electroencephalogram (EEG) is a useful method in clinical applications, such as the study of localized epilepsy, evoked-related-potentials, and attention deficit/hyperactivity disorder. The distributed-source model is a common method to estimate neural activity in the brain. The location and amplitude of each active source are estimated by solving the inverse problem by regularization or using Bayesian methods with spatio-temporal constraints.

Potato native and wound periderms are differently affected by down-regulation of FHT, a suberin feruloyl transferase.

Potato native and wound healing periderms contain an external multilayered phellem tissue (potato skin) consisting of dead cells whose cell walls are impregnated with suberin polymers. The phellem provides physical and chemical barriers to tuber dehydration, heat transfer, and pathogenic infection. Previous RNAi-mediated gene silencing studies in native periderm have demonstrated a role for a feruloyl transferase (FHT) in suberin biosynthesis and revealed how its down-regulation affects both chemical composition and physiology.