Seizures

Supervised autoencoder for gradient and BCG artifact removal in EEG during simultaneous EEG-fMRI.

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary views of brain function, but MRI-induced gradient (GA) and ballistocardiogram (BCG) artifacts can overwhelm the EEG. We propose Deep Artifact Removal (DAR), a supervised 1D convolutional autoencoder trained on paired artifact-contaminated and MR-corrected segments from the public Carbon...

A Dataset of Microelectrode Recordings from Deep Brain Stimulation Procedures.

Precise intraoperative localisation of subcortical brain structures remains a critical challenge in deep brain stimulation, yet openly available microelectrode recording datasets are scarce. We present a dataset of 6,646 processed MER recordings from 132 patients with neurological disorders, including Parkinson's disease, dystonia, Huntington's disease, epilepsy and others, acquired during DBS pro...

Transformer-based emotion recognition in interactive art: A multimodal neural approach.

Understanding how interactive digital art affects emotional states is essential to advance research into the interface between affective neuroscience ...

Development and validation of an explainable prediction model for post-stroke epilepsy in patients with ischaemic stroke following mechanical thrombectomy: a multicentre retrospective cohort study.

OBJECTIVE: In this retrospective multicentre cohort study, we aimed to develop and validate an interpretable machine learning (ML) model for early pos...

Distinct mental fatigue mechanisms under prolonged work and night shifts Implications for safety management in a real-world oil drilling site.

OBJECTIVE: This study aimed to investigate how shift work and prolonged working hours affect mental fatigue under real-world working conditions, focus...

Integrating weighted gene co-expression network analysis and machine learning reveal that NDUFA4L2 alleviates KA-induced HT22 cell neurotoxicity, apoptosis, oxidative stress, and mitochondrial dysfunction.

Temporal lobe epilepsy (TLE) is one of the most common types of epilepsy, with frequent seizures often leading to cognitive, emotional, and psychiatri...

Deep learning based depth of anaesthesia monitoring using EEG: a 4-layer CNN model with PSD and BSR correlation features.

Monitoring the depth of anaesthesia (DoA) through electroencephalogram (EEG) analysis plays a major role in maintaining patient safety and guiding opt...

A multimodal epilepsy dataset of paired 3-Tesla and 7-Tesla MRI and intracranial EEG.

There is an increasing need to integrate multimodal datasets in epilepsy research, particularly to correlate electrophysiology with imaging in patient...

BIDS-formatted resting-state and loudness dependence of auditory evoked potentials (LDAEP) EEG dataset from healthy women.

Here, we describe a publicly available electroencephalography (EEG) dataset recorded from 69 healthy women (aged 19-40). Data were acquired using a 64...

Quantitative sleep EEG identifies CSF core biomarker-related subgroups in Alzheimer's disease.

Early detection and biological characterization of Alzheimer's disease (AD) remain challenging, as current diagnostic approaches rely on invasive cere...

Use of artificial intelligence in magnetic resonance imaging across the epileptic patient's journey: A meta-analysis of four clinical applications.

OBJECTIVE: The application of artificial intelligence/machine learning (AI/ML) to magnetic resonance imaging (MRI) promises to enhance and support cli...

Raw EEG as a viable alternative to engineered decompositions in anesthetic depth prediction.

PURPOSE: Assessing the depth of anesthesia remains a challenge in operating rooms worldwide, as hospitals often rely on proprietary monitors that are ...

Robust multi-centre interictal EEG biomarker for distinguishing epilepsy from mimickers.

OBJECTIVE: To develop and validate an interpretable multi-centre interictal EEG biomarker for distinguishing epilepsy from mimickers, addressing the c...

Prediction of an fMRI-based schizophrenia biomarker from EEG using dynamic functional connectivity: a simultaneous EEG-fMRI study.

OBJECTIVE: Recent advances in functional magnetic resonance imaging (fMRI) have identified brain functions associated with psychiatric disorders using...

Resting-state EEG microstates as biomarkers for major depressive disorder.

OBJECTIVES: To investigate the temporal dynamics of resting-state electroencephalography (EEG) microstates in patients with Major Depressive Disorder ...

EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network.

Emotion recognition from EEG signals has been one of the most promising areas due to its potential in enhancing human-computer interaction, especially...

Neural Response to Familiar Names Predicts Outcome of Comatose ICU Patients: A Prospective Observational Cohort Study.

Predicting the outcome of comatose patients in the intensive care unit (ICU) can inform decision making but remains challenging. Recent studies sugges...

Comparison of volume- and surface-based magnetic resonance imaging morphometry algorithms in the detection of focal cortical dysplasia.

OBJECTIVE: Detection of focal cortical dysplasia (FCD) remains a major challenge in presurgical epilepsy diagnostics. Magnetic resonance imaging (MRI)...

A Hybrid Deep Learning Framework with Supervised Contrastive Learning for Robust Seizure Detection in Long-Term EEG.

Accurate and timely automatic detection of epileptic seizures is crucial for reducing the workload for visually inspecting long-term electroencephalog...

TRIDENT: A multi-task, triple-branch deep learning framework for EEG-based recognition, severity estimation, and future high-anger prediction in an on-road Wizard-of-Oz paradigm.

Driving anger is strongly associated with aggressive driving and elevated crash risk. However, continuous modeling of graded anger-related affective s...