Seizures

Topographic differences in EEG microstates: distinguishing juvenile myoclonic epilepsy from frontal lobe epilepsy.

UNLABELLED: This study aims to develop an exploratory classification model for Juvenile Myoclonic Epilepsy (JME) based on electroencephalogram (EEG) microstate features to assist clinical diagnosis and reduce misdiagnosis rates. A total of 123 participants were included in this study, consisting of 74 patients diagnosed with JME and 49 patients with Frontal Lobe Epilepsy (FLE). Resting-state EEG d...

HBUED: An EEG dataset for emotion recognition.

Emotion recognition via electroencephalogram (EEG) data is crucial for improving human-computer interaction. In practice, researchers require a substantial quantity of EEG samples to train and validate models. However, existing EEG datasets typically have a limited number of subjects. To address this issue, we present a large-scale EEG dataset, the Hebei University Emotional EEG Dataset (HBUED), s...

Abnormalities of brain dynamics based on large-scale cortical network modeling in autism spectrum disorder.

Synaptic increase is a common phenomenon in the brain of autism spectrum disorder (ASD). However, the impact of increased synapses on the neurophysiol...

The interaction of UBR4, LRP1, and OPHN1 in refractory epilepsy: Drosophila model to investigate the oligogenic effect on epilepsy.

Refractory epilepsy is an intractable neurological disorder that can be associated with oligogenic/polygenic etiologies. Through trio-based whole-exom...

A subject transfer neural network fuses Generator and Euclidean alignment for EEG-based motor imagery classification.

BACKGROUND: Brain-computer interface (BCI) facilitates the connection between human brain and computer, enabling individuals to control external devic...

EEG-based prediction of reaction time during sleep deprivation.

Prolonged wakefulness is known to adversely affect basic cognitive abilities such as object recognition and decision-making. It affects the dynamics o...

Interpretable EEG-to-Image Generation with Semantic Prompts

Decoding visual experience from brain signals offers exciting possibilities for neuroscience and interpretable AI. While EEG is accessible and tempo...

eegFloss: A Python package for refining sleep EEG recordings using machine learning models

Electroencephalography (EEG) allows monitoring of brain activity, providing insights into the functional dynamics of various brain regions and their...

From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Visual Concepts in Brain Signal Analysis

EEG signals capture brain activity with high temporal and low spatial resolution, supporting applications such as neurological diagnosis, cognitive ...

Deep learning on high-density EEG during a cognitive task distinguishes patients with Parkinson's disease from healthy controls.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, including cognitive impairment. Its diagnosis,...

Annotating neurophysiologic data at scale with optimized human input.

Neuroscience experiments and devices are generating unprecedented volumes of data, but analyzing and validating them presents practical challenges, pa...

A novel STA-EEGNet combined with channel selection for classification of EEG evoked in 2D and 3D virtual reality.

Virtual reality (VR), particularly through 3D presentations, significantly boosts user engagement and task efficiency in fields such as gaming, educat...

Application of EEG microstates in Parkinson's disease.

Electroencephalography (EEG) microstate analysis is a promising technique for detecting transient brain dynamics and identifying disease-specific biom...

Augmenting Common Spatial Patterns to deep learning networks for improved alcoholism detection using EEG signals.

One of the main risk factors for numerous health problems is excessive drinking. Alcoholism is a severe disorder that can affect a person's thinking a...

Self-training EEG discrimination model with weakly supervised sample construction: An age-based perspective on ASD evaluation.

Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination and evaluation of brain disorders. However, despite ...

EEG-Based Auditory BCI for Communication in a Completely Locked-In Patient Using Volitional Frequency Band Modulation

Patients with amyotrophic lateral sclerosis (ALS) in the completely locked-in state (CLIS) can lose all reliable motor control and are left without ...

CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding

Understanding and decoding brain activity from electroencephalography (EEG) signals is a fundamental challenge in neuroscience and AI, with applicat...

3D-Telepathy: Reconstructing 3D Objects from EEG Signals

Reconstructing 3D visual stimuli from Electroencephalography (EEG) data holds significant potential for applications in Brain-Computer Interfaces (B...

Mobile Sleep Stage Analysis Using Multichannel Wearable Devices Integrated with Stretchable Transparent Electrodes.

The prevalence of sleep disorders in the aging population and the importance of sleep quality for health have emphasized the need for accurate and acc...

[Study on speech imagery electroencephalography decoding of Chinese words based on the CAM-Net model].

Speech imagery is an emerging brain-computer interface (BCI) paradigm with potential to provide effective communication for individuals with speech im...