In this paper, we propose a novel deep learning framework, called spatial-temporal recurrent neural network (STRNN), to integrate the feature from both spatial and temporal information of signal sources into unified dependency model. STRNN, capture those spatially co-occurrent variations human emotions, multidirectional (RNN) layer is employed long-range contextual cues by traversing regions each slice along different directions. Then bi-directional RNN further used learn discriminative...

In this paper, we propose a novel neural network model, called bi-hemisphere domain adversarial (BiDANN) for electroencephalograph (EEG) emotion recognition. The BiDANN model is inspired by the neuroscience findings that left and right hemispheres of human's brain are asymmetric to emotional response. It contains global two local discriminators work adversarially with classifier learn discriminative features each hemisphere. At same time, it tries reduce possible differences in hemisphere...

Neuroscience study has revealed the discrepancy of emotion expression between left and right hemispheres human brain. Inspired by this study, in article, we propose a novel bi-hemispheric model (BiHDM) to learn information two improve electroencephalograph (EEG) recognition. Concretely, first employ four directed recurrent neural networks (RNNs) based on spatial orientations traverse electrode signals separate brain regions. This enables proposed obtain deep representations all EEG...

In this paper, we propose a novel Electroencephalograph (EEG) emotion recognition method inspired by neuroscience with respect to the brain response different emotions. The proposed method, denoted R2G-STNN, consists of spatial and temporal neural network models regional global hierarchical feature learning process learn discriminative spatial-temporal EEG features. To features, bidirectional long short term memory (BiLSTM) is adopted capture intrinsic relationships electrodes within region...

Previous electroencephalogram (EEG) emotion recognition relies on single-task learning, which may lead to overfitting and learned features lacking generalization. In this paper, a graph-based multi-task self-supervised learning model (GMSS) for EEG is proposed. GMSS has the ability learn more general representations by integrating multiple tasks, including spatial frequency jigsaw puzzle contrastive tasks. By from tasks simultaneously, can find representation that captures all of thereby...

The automatic seizure detection system can effectively help doctors to monitor and diagnose epilepsy thus reducing their workload. Many outstanding studies have given good results in the two-class problems, but most of them are based on hand-wrought feature extraction. This study proposes an end-to-end deep learning, which does not require heavy preprocessing EEG data or engineering. fully convolutional network with three convolution blocks is first used learn expressive characteristics from...

Emotion recognition from electroencephalograph (EEG) signals has long been essential for affective computing. In this article, we evaluate EEG emotion by converting multiple channels into images such that richer spatial information can be considered and the question of EEG-based converted image recognition. To end, propose a novel method to generate continuous discrete introducing offset variables following Gaussian distribution each channel alleviate biased electrode coordinates during...

The individual differences and the dynamic uncertain relationships among different electroencephalogram (EEG) regions are essential factors that limit EEG emotion recognition. To address these issues, in this article, we propose a variational instance-adaptive graph method (V-IAG) simultaneously captures dependencies electrodes estimates underlying information. Specifically, employ two branches, i.e., branch branch, to construct graph. Inspired by attention mechanism, generates based on...

In this paper, we propose a novel domain invariant feature learning (DIFL) method to deal with speaker-independent speech emotion recognition (SER). The basic idea of DIFL is learn the speaker-invariant by eliminating shifts between training and testing data caused different speakers from perspective multi-source unsupervised adaptation (UDA). Specifically, embed hierarchical alignment layer strong-weak distribution strategy into extraction block firstly reduce discrepancy in distributions...

ABSTRACT At present, the research to predict efficacy of tacrolimus (TAC) mainly focuses on serological indexes and urine analysis. Because these indicators are affected by many factors, they cannot accurately therapeutic effect primary membranous nephropathy (PMN) patients. In this study, a novel classification model (RCN) based hyperspectral imaging combined with one‐dimensional convolutional neural networks (1D CNN) relevance vector machine (RVM) was proposed for predicting patients'...

How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and importance these features in staging are not fully understood. This study aimed to investigate RR interval time series explore their values staging. We performed approximate entropy (ApEn), sample (SampEn), fuzzy (FuzzyEn), distribution (DistEn), conditional (CE), permutation (PermEn) analyses on extracted from epochs that were constructed based two methods: (1) 270-s epoch length (2)...

The dopamine transporter shapes dopaminergic neurotransmission by clearing extracellular and replenishing vesicular stores. carries an endogenous binding site for Zn2+, but the nature of Zn2+-dependent modulation has remained elusive: both, inhibition stimulation DAT have been reported. Here, we exploited high time resolution patch-clamp recordings to examine effects Zn2+ on transport cycle DAT: recorded peak currents associated with substrate translocation steady-state reflecting forward...

Objective.Directly decoding imagined speech from electroencephalogram (EEG) signals has attracted much interest in brain-computer interface applications, because it provides a natural and intuitive communication method for locked-in patients. Several methods have been applied to decoding, but how construct spatial-temporal dependencies capture long-range contextual cues EEG better decode should be considered.Approach.In this study, we propose novel model called hybrid-scale dilated...

The human dopamine transporter (DAT) has a tetrahedral Zn2+-binding site. sites are also recognized by other first-row transition metals. Excessive accumulation of manganese or copper can lead to parkinsonism because deficiency. Accordingly, we examined the effect Mn2+, Co2+, Ni2+, and Cu2+ on transport-associated currents through DAT DAT-H193K, mutant with disrupted All metals except Mn2+ modulated transport cycle wild-type affinities in low micromolar range. In this concentration range,...

An electroencephalogram (EEG)-based motor imagery (MI) brain–computer interface (BCI) builds a direct communication channel between humans and computers by decoding EEG signals. The intersubject ability is crucial for the application of MI-BCI, which implies that subject can use MI-BCI equipment without recording additional data training. Physiologically, because distinction in method, brain structure, state, distribution MI different. This often leads to partial or even complete failure...

Abstract Two kinds of structural characterization method as local descriptors and global were used to parameterize peptide structures, several quantitative structure‐retention relationship models then constructed using partial least square (PLS), least‐squares support vector machine (LS‐SVM) Gaussian process (GP) coupled with genetic algorithm‐variable selection. These validated rigorously investigated systematically by Tropsha et al. criteria, Monte Carlo cross‐validation one‐way analysis...

Abstract Objective. The rapid serial visual presentation (RSVP) paradigm, which is based on the electroencephalogram (EEG) technology, an effective approach for object detection. It aims to detect event-related potentials (ERP) components evoked by target images identification. However, detection performance within this paradigm affected disparity between adjacent in a sequence. Currently, there no objective metric quantify difference. Consequently, reliable image sorting method required...