Seizures

Cross-subject emotion recognition with loop adaptive adversarial transfer network.

Emotion recognition has broad application prospects in real life. The variability across subjects in emotion-related electroencephalogram (EEG) signals is still a significant challenge for the practical use of EEG-based emotion recognition despite recent findings that suggest EEG signals are informative and beneficial for recognizing emotions. This work proposes a loop adaptive adversarial transfe...

Fuzzy symbolic convergent cross mapping: A causal coupling measure for EEG signals in disorders of consciousness patients.

Accurate and timely diagnosis in disorders of consciousness (DOC) patients remains a core clinical challenge. Electroencephalography (EEG) shows strong potential for detecting physiological biomarkers of consciousness, and brain network analysis serves as an effective technique. Therefore, a robust approach to brain network construction is of great significance. The convergent cross mapping (CCM) ...

Unconscious elevated bottom-up processing in depression: Insights from dynamic causal modeling with EEG and fMRI.

INTRODUCTION: MRI compatible EEG systems enable simultaneous EEG-fMRI data assessment, which provides high spatial and high temporal resolution of neu...

Revealing EEG signatures of intervention in disorder of consciousness using artificial intelligence: methodology and feasibility.

BACKGROUND AND OBJECTIVE: Electroencephalography (EEG) is a crucial tool for monitoring recovery in patients with disorders of consciousness (DOC) aft...

Neurovista: A bidirectional masked cross-Modal fusion network for robust EEG-to-Image decoding.

Electroencephalography (EEG)-based visual decoding has significant potential in brain-computer interfaces but faces substantial challenges due to nois...

TurkerPat: EEG-Based detection of hunger, thirst, and nicotine withdrawal.

BACKGROUND: One of the primary objectives of neuroscience is to gather information from the brain. Therefore, brain data are crucial for understanding...

Multiomic insights into the MPO-mediated NET formation pathway in alcohol-induced epilepsy risk.

Epilepsy is a highly prevalent chronic central nervous system disorder that imposes substantial societal and economic burdens. Inconsistent associatio...

MSDA-Net: Multiscale Spatiotemporal Dual-Attention Network for EEG-Based Driver Fatigue Detection.

Driver fatigue poses a severe risk to road safety, contributing to approximately 20% of fatal accidents worldwide. While EEG signals are the gold stan...

BACNet: A multi-attention network for cross-subject and cross-task EEG-based pilot operational intent recognition.

BACKGROUND AND OBJECTIVE: Recognizing pilot operational intent is crucial for enhancing flight safety and improving the efficiency of human-machine in...

Biased minds, sensitive hearts: Divergent neural signatures of interpersonal sensitivity in atypical and non-atypical depression.

BACKGROUND: Atypical depression (AD) is a distinct subtype of depression, with interpersonal sensitivity as one of its core characteristics. However, ...

Closed-Loop Control of Epilepsy Based on Reinforcement Learning.

This study proposes a novel adaptive DBS control strategy for epilepsy treatment based on deep reinforcement learning. By establishing a random distur...

An adaptive decoupling learning system informed by the brain functional structure for EEG decoding.

Neuroscientific investigations have revealed the presence of regional pathway connections among functional brain areas, as well as the asymmetrical st...

Neurophysiological mechanisms and predictive modeling of SSRI treatment response in depression disorder based on multidimensional EEG features.

BACKGROUND: Depression exhibits significant heterogeneity in antidepressant treatment response. This study aimed to develop an Electroencephalography ...

Brain dynamics and depressive symptoms in young adults: Evidence from EEG.

BACKGROUND: Depression is a major public health concern, with a rising prevalence among adolescents and young adults. However, the neural mechanisms u...

CNN-Autoformer: Automated EEG-Based Seizure Detection and Localization Using Hybrid Deep Learning.

Epilepsy is a neurological disorder characterized by transient and recurrent abnormal brain activity, often diagnosed through manual inspection extens...

Distinct electroencephalogram microstate in patients with methamphetamine use disorder and obsessive-compulsive disorder.

BACKGROUND: Electroencephalogram (EEG) microstates reflect momentary localized brain activity and may indicate spontaneous fluctuations within large-s...

Visually-Inspired Multimodal Iterative Attentional Network for High-Precision EEG-Eye-Movement Emotion Recognition.

Advancements in artificial intelligence have propelled affective computing toward unprecedented accuracy and real-world impact. By leveraging the uniq...

A novel machine learning-based method to quantify the effect of transcranial direct current stimulation on opioid users.

BACKGROUND: Opioid addiction is a major public health concern, associated with numerous health and social problems. Conventional diagnostic methods fo...

Structure and function of voltage-gated sodium channel Nav1.6: Involvement in the pathological process of neural injury.

The voltage-gated sodium channel Nav1.6, encoded by the sodium voltage-gated channel alpha subunit 8 gene, is a crucial regulator of neuronal excitabi...

A multi-level teacher assistant-based knowledge distillation framework with dynamic feedback for motor imagery EEG decoding.

Deep learning has shown promise in motor imagery-based electroencephalogram (MI-EEG) decoding, a critical task in non-invasive brain-computer interfac...