Seizures

Combining EEG, event-related potentials, and MRI biomarkers for detection of mild cognitive impairment: A machine learning approach.

OBJECTIVE: Mild cognitive impairment (MCI) is an intermediary stage between typical cognitive aging and dementia. Identifying reliable biomarkers for early detection of MCI is crucial for slowing disease progression. This study explored multimodal biomarkers associated with amyloid-positive MCI and assessed wearable EEG/ERP and MRI features using machine learning. METHODS: This study included 70 p...

Adaptive multimodal learning for driver cognitive state monitoring using transformer-based fusion with personalized meta-learning and federated optimization.

Road accidents caused by driver fatigue and cognitive overload remain a significant public safety concern. According to recent traffic safety data, drowsy driving contributes to thousands of fatal accidents each year, emphasizing the urgent need for intelligent driver monitoring systems. To address this, we propose an adaptive multimodal deep learning framework (AML) for real-time cognitive worklo...

EEG-based harmful brain activity classification using deep learning and feature fusion.

The prevalence of research on harmful brain activity has increased, especially since the standardization of electroencephalography (EEG) terminologies...

Decoding the neural mechanisms of salty peptide perception via electroencephalography and machine learning.

Excessive sodium intake poses major public health risks, driving the search for salt substitutes that preserve desirable flavor. Salty peptides have e...

Identification of EEG features during status epilepticus for prediction of emergent epilepsy phenotype in the mouse intra-amygdala kainic acid model using supervised learning.

Preclinical animal models are essential for investigating epilepsy mechanisms and evaluating novel therapies. In rodents, epilepsy can be induced by s...

Federated learning-enabled privacy-preserving framework for seizure forecasting and affective state analysis using multi-modal EEG-ECG data.

Seizure forecasting and affective state analysis using EEG-ECG data play a pivotal role in advancing neurological and mental health monitoring. Howeve...

Self-Supervised Contrastive Pre-Training for EEG-Based Recognition via Cross Device Representation Consistency.

Electroencephalography (EEG) has emerged as a powerful tool for modeling human brain states. However, the widespread adoption of EEG-based recognition...

SemSTNet: Medical EEG Semantic Metric Learning With Class Prototypes Generated by Pretrained Language Model.

Electroencephalography (EEG) feature learning is crucial for brain-machine interfaces and medical diagnostics. Existing deep learning models for class...

Interpretable Machine Learning to Anticipate the Diagnostic Yield of EEG in the Emergency department. The EMINENCE study.

INTRODUCTION: Emergent electroencephalography (emEEG) is increasingly employed in the emergency department (ED) for evaluating altered consciousness a...

Deep learning for EEG-based sleep stage classification: a review.

Deep learning architectures are now widely applied in sleep electroencephalogram (EEG) analysis. These developments have significantly advanced EEG-ba...

Microstate permutation complexity of EEG signals distinguishes minimally conscious state plus from minimally conscious state minus.

BACKGROUND: Accurately distinguishing minimally conscious state plus (MCS+) from minimally conscious state minus (MCS-) is critical for prognosis and ...

Subject-Independent Deep Learning Framework for Motor Imagery Electroencephalogram Decoding in Neurorehabilitation.

Motor imagery (MI) has emerged as a pivotal paradigm in non-invasive brain-computer interfaces (BCIs) for neurorehabilitation, enabling motor function...

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models.

Event-related potential (ERP), a specialized paradigm of electroencephalographic (EEG), reflects neurological responses to external stimuli or events,...

Complexity of resting cortical activity predicts neurophysiological responses to theta-burst stimulation but fails to generalize: A rigorous machine-learning approach.

BACKGROUND: Substantial variability in individual responses to intermittent theta-burst stimulation (iTBS) limits its clinical efficacy, yet neurophys...

Exploratory decoding of TMS-EEG: Predicting TEP response to intermittent and continuous theta burst stimulation.

Theta burst stimulation (TBS) is a promising form of repetitive transcranial magnetic stimulation (rTMS) capable of modulating cortical excitability a...

Explainable artificial intelligence-driven visual task-specific electroencephalogram analysis for attention deficit hyperactivity disorder detection using information-theoretic feature selection.

Attention deficit hyperactivity disorder (ADHD) is a neurological disorder that primarily develops in early childhood and affects motor development, v...

Can epilepsy be predicted after the first febrile seizure? Insights from machine learning of postictal EEG.

OBJECTIVE: Febrile seizures (FS) are the most common seizures in childhood, yet identifying children at risk of developing epilepsy after the first FS...

EEG-VLM: A Hierarchical Vision-Language Model With Multi-Level Feature Alignment and Visually Enhanced Language-Guided Reasoning for EEG Image-Based Sleep Stage Prediction.

Sleep stage classification based on electroencephalography (EEG) is fundamental for assessing sleep quality and diagnosing sleep-related disorders. Ho...

Neural estimates of language comprehension of sentences with selected action verbs in a non-literal context in the Polish language studied by permutation cluster-based analysis and decoding of the event-related potentials.

BACKGROUND: This work examines how the human brain processes mental metaphors in Polish verbal phraseologisms (a fixed, non-compositional combination ...

Frequency Band Personalization for Seizure Network Analysis in Multifocal Patients.

Stereo-electroencephalography (SEEG) is commonly used for pre-surgical evaluation in patients with multifocal epilepsy undergoing responsive neurostim...