The association between social rewards and anxiety: Links from neurophysiological analysis in virtual reality and social interaction game.

Individuals' affective experience can be intricate, influenced by various factors including monetary rewards and social factors during social interaction. However, within this array of factors, divergent evidence has been considered as potential contributors to social anxiety. To gain a better understanding of the specific factors associated with anxiety during social interaction, we combined a social interaction task with neurophysiological recordings obtained through an anxiety-elicitation task conducted in a Virtual Reality (VR) environment.

Sleep deprivation changes frequency-specific functional organization of the resting human brain.

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.

Effect of Phase Clustering Bias on Phase-Amplitude Coupling for Emotional EEG.

Background: Emotions are thought to be related to distinct patterns of neural oscillations, but the interactions among multi-frequency neural oscillations during different emotional states lack full exploration. Phase-amplitude coupling is a promising tool for understanding the complexity of the neurophysiological system, thereby playing a crucial role in revealing the physiological mechanisms underlying emotional electroencephalogram (EEG). However, the non-sinusoidal characteristics of EEG lead to the non-uniform distribution of phase angles, which could potentially affect the analysis of phase-amplitude coupling.

Fusion of Multi-domain EEG Signatures Improves Emotion Recognition.

Background: Affective computing has gained increasing attention in the area of the human-computer interface where electroencephalography (EEG)-based emotion recognition occupies an important position. Nevertheless, the diversity of emotions and the complexity of EEG signals result in unexplored relationships between emotion and multichannel EEG signal frequency, as well as spatial and temporal information.

Methods: Audio-video stimulus materials were used that elicited four types of emotions (sad, fearful, happy, neutral) in 32 male and female subjects (age 21-42 years) while collecting EEG signals.

Classification of EEG Signals Based on Sparrow Search Algorithm-Deep Belief Network for Brain-Computer Interface.

In brain-computer interface (BCI) systems, challenges are presented by the recognition of motor imagery (MI) brain signals. Established recognition approaches have achieved favorable performance from patterns like SSVEP, AEP, and P300, whereas the classification methods for MI need to be improved. Hence, seeking a classification method that exhibits high accuracy and robustness for application in MI-BCI systems is essential.

Multimodal Vigilance Estimation With Modality-Pairwise Contrastive Loss.

Nowadays, how to estimate vigilance with higher accuracy has become a hot field of research direction. Although the increasing available modalities opens the door for amazing new possibilities to achieve good performance, the uncertain cross-modal interaction still poses a real challenge to the multimodal fusion. In this paper, a cross-modality alignment method has been proposed based on the contrastive learning for extracting shared but not the same information among modalities.

The increasing instance of negative emotion reduce the performance of emotion recognition.

Introduction: Emotion recognition plays a crucial role in affective computing. Recent studies have demonstrated that the fuzzy boundaries among negative emotions make recognition difficult. However, to the best of our knowledge, no formal study has been conducted thus far to explore the effects of increased negative emotion categories on emotion recognition.

An Improved Canonical Correlation Analysis for EEG Inter-Band Correlation Extraction.

(1) Background: Emotion recognition based on EEG signals is a rapidly growing and promising research field in affective computing. However, traditional methods have focused on single-channel features that reflect time-domain or frequency-domain information of the EEG, as well as bi-channel features that reveal channel-wise relationships across brain regions. Despite these efforts, the mechanism of mutual interactions between EEG rhythms under different emotional expressions remains largely unexplored.

Chronic corticosterone exposure impairs emotional regulation and cognitive function through disturbing neural oscillations in mice.

Corticosterone is a stress hormone, which is often associated with a variety of the central nervous system diseases. The study was to investigate the effects of Chronic corticosterone exposure (CCE) on the alteration of neural oscillatory patterns which supported a wide range of basic and higher cognitive activities, and a potential mechanism. Accordingly, a chronic corticosterone exposure model was established in C57BL mice.

A Feed-Forward Neural Network for Increasing the Hopfield-Network Storage Capacity.

In the hippocampal dentate gyrus (DG), pattern separation mainly depends on the concepts of 'expansion recoding', meaning random mixing of different DG input channels. However, recent advances in neurophysiology have challenged the theory of pattern separation based on these concepts. In this study, we propose a novel feed-forward neural network, inspired by the structure of the DG and neural oscillatory analysis, to increase the Hopfield-network storage capacity.

Spatial localization in target detection based on decoding N2pc component.

Background: The Gaze-independent BCI system is used to restore communication in patients with eye movement disorders. One available control mechanism is the utilization of spatial attention. However, spatial information is mostly used to simply answer the "True/False" target recognition question and is seldom used to improve the efficiency of target detection.

Adaptive asynchronous control system of robotic arm based on augmented reality-assisted brain-computer interface.

. Brain-controlled robotic arms have shown broad application prospects with the development of robotics, science and information decoding. However, disadvantages, such as poor flexibility restrict its wide application.

Application of expert system and LSTM in extracting index of synaptic plasticity.

The indexes of synaptic plasticity, including long-term potentiation (LTP) and long-term depression (LTD), can usually be measured by evaluating the slope and/or magnitude of field excitatory postsynaptic potentials (fEPSPs). So far, the process depends on manually labeling the linear portion of fEPSPs one by one, which is not only a subjective procedure but also a time-consuming job. In the present study, a novel approach has been developed in order to objectively and effectively evaluate the index of synaptic plasticity.

Early-stage dysfunction of hippocampal theta and gamma oscillations and its modulation of neural network in a transgenic 5xFAD mouse model.

Alzheimer's disease (AD) is pathologically characterized by amyloid-β (Aβ) accumulation, which induces Aβ-dependent neuronal dysfunctions. We focused on the early-stage disease progression and examined the neuronal network functioning in the 5xFAD mice. The simultaneous intracranial recordings were obtained from the hippocampal perforant path (PP) and the dentate gyrus (DG).

ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition.

Electroencephalogram (EEG) signals recorded from sensor electrodes on the scalp can directly detect the brain dynamics in response to different emotional states. Emotion recognition from EEG signals has attracted broad attention, partly due to the rapid development of wearable computing and the needs of a more immersive human-computer interface (HCI) environment. To improve the recognition performance, multi-channel EEG signals are usually used.