This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference time series, phase, normalized energy. The performance proposed verified publicly available emotional database. results show that three features effective recognition. role each inquired we find...

High-frequency electroencephalography (EEG) signals play an important role in research on human emotions. However, the different network patterns under emotional states high gamma band (50-80 Hz) remain unclear. In this paper, we investigate using functional analysis various frequency bands. We constructed multiple networks bands and performed time-frequency these to determine significant features that represent states. Furthermore, verified effectiveness of by them emotion recognition. Our...

Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) is a prospective tool to enhance the emotion regulation capability of participants and alleviate their emotional disorders. The hippocampus key brain region in network plays significant role social cognition processing brain. However, few studies have focused on NF hippocampus. This study investigated feasibility training healthy self-regulate activation assessed effect rtfMRI-NF before after training. Twenty-six...

Toll-like receptor 4 (TLR4) recognizes specific structural motifs associated with microbial pathogens and also responds to certain endogenous host molecules tissue damage. In Duchenne muscular dystrophy (DMD), inflammation plays an important role in determining the ultimate fate of dystrophic muscle fibers. this study, we used TLR4-deficient mdx mice assess TLR4 pathogenesis DMD. expression was increased showed enhanced activation following agonist stimulation diaphragm muscle. Genetic...

The electroencephalogram (EEG) signal represents a subject’s specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention. However, the accuracy stability of current EEG-based person authentication systems are still unsatisfactory in practical application. In this paper, multi-task system combining eye blinking proposed, which can achieve high precision robustness. Firstly, we design novel evoked paradigm using self- or non-self-face rapid...

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of patterns in self-induced emotions remains uninvestigated. In this study we inferred signatures from electroencephalogram (EEG) signals. The EEG 30 participants were recorded while they watched 18 Chinese movie clips which intended elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger fear. After...

Emotion recognition plays an important part in human-computer interaction (HCI). Currently, the main challenge electroencephalogram (EEG)-based emotion is non-stationarity of EEG signals, which causes performance trained model decreasing over time. In this paper, we propose a two-level domain adaptation neural network (TDANN) to construct transfer for EEG-based recognition. Specifically, deep features from topological graph, preserve information are extracted using network. These then passed...

Abstract Peripheral nerve injury (PNI) remains an intractable challenge in regenerative medicine. Recently, physical cue‐based strategies (e.g., electrical neurostimulation, acoustic radiation, electromagnetic bioregulation, as well directional fiber guiding, etc.) have drawn increasing attention not only a stimulator for cell functions modulation and fate determination, but also morphology‐index modulating phenotype, proliferation, differentiation, especially cells. More importantly, the...

The mechanisms of highland barley whole grain (BWG) with rich phenolics on obese db/db mice were investigated in this study. Oral consumption BWG reduced food intake, body weight, organ/body weight indexes liver and fat, levels serum hepatic lipids, injury, oxidative stress. Furthermore, recovered the disorder cecal microbiota by augmenting Bacteroidetes/Firmicutes ratio Alistipes abundance decreasing abundances Bacteroides Desulfovibrionaceae to modulate lipid metabolism-related genes....

Electroencephalography (EEG)-based emotion computing has become one of the research hotspots human-computer interaction (HCI). However, it is difficult to effectively learn interactions between brain regions in emotional states by using traditional convolutional neural networks because there information transmission neurons, which constitutes network structure. In this paper, we proposed a novel model combining graph and network, namely MDGCN-SRCNN, aiming fully extract features channel...

Abstract The impact of excessive cognitive workload on personal work and life is widely recognized, yet the brain information processing mechanisms under overload remain unclear. This study employed a spatial configuration task, combined with time-varying network analysis source localization techniques based electroencephalography signals, to delve into dynamic adjustment processes among healthy participants during overload. results revealed that overload, overall activation level...

Electroencephalogram (EEG) signals, which originate from neurons in the brain, have drawn considerable interests identity authentication. In this paper, a face image-based rapid serial visual presentation (RSVP) paradigm for authentication is proposed. This combines two kinds of biometric trait, and EEG, together to evoke more specific stable traits The event-related potential (ERP) components induced by self-face non-self-face (including familiar not familiar) are investigated, significant...

Little is known about the relationship between diet and depression through gut microbiota among breast cancer patients. This study aimed to examine dietary intake differences depressed (DBC) non-depressed (NBC) patients, whether could lead changes that affect depressive symptoms. Participants completed Center for Epidemiological Studies-Depression Scale (CES-D) 24 h recall. Fecal samples of 18 DBC patients 37 NBC were collected next-generation sequencing. A total 60 out 205 reported...

In neuroscience, all kinds of computation models were designed to answer the open question how sensory stimuli are encoded by neurons and conversely, can be decoded from neuronal activities. Especially, functional Magnetic Resonance Imaging (fMRI) studies have made many great achievements with rapid development deep network computation. However, comparing goal decoding orientation, position object category human fMRI in visual cortex, accurate reconstruction image is a still challenging...

Recently, visual encoding and decoding based on functional Magnetic Resonance Imaging (fMRI) have made many great achievements with the rapid development of deep network computation. In spite hierarchically similar representations human vision, information flows from primary cortices to high cortices, conversely, respectively bottom-up manner top-down manner. Inspired by bidirectional flows, we proposed recurrent neural (BRNN) method decode category fMRI data. The forward backward directions...

One of the greatest limitations in field EEG-based emotion recognition is lack training samples, which makes it difficult to establish effective models for recognition. Inspired by excellent achievements generative image processing, we propose a data augmentation model named VAE-D2GAN using adversarial network. EEG features representing different emotions are extracted as topological maps differential entropy (DE) under five classical frequency bands. The proposed designed learn...

The electroencephalogram (EEG) signal represents a subject's specific brain activity patterns and is considered as an ideal biometric given its superior invisibility, non-clonality, non-coercion. In order to enhance applicability in identity authentication, novel EEG-based authentication method proposed based on self- or non-self-face rapid serial visual presentation. contrast previous studies that extracted EEG features from rest state motor imagery, the designed paradigm could obtain...