Seizures

Discriminating between major depressive disorder and bipolar depression: Aberrant EEG microstate dynamics and machine learning classification.

BACKGROUND: Major depressive disorder (MDD) and bipolar depression (BD) are common mood disorders with overlapping clinical features, posing significant challenges for accurate diagnosis and effective treatment. Electroencephalography (EEG) microstates reflect transient, quasi-stable patterns of brain activity that index fast, large-scale neural network dynamics and may provide novel insights into...

Prioritising follow-up for people with suspected epilepsy using a digital EEG biomarker.

Lengthy waits for follow-up testing are common for people with suspected epilepsy. This delays diagnosis, prolongs uncertainty and increases seizure risk. Initial EEGs are frequently inconclusive, yet follow-ups are often dictated by referral date, and there is no established method for risk-based prioritisation. Here, we tested whether an established digital EEG biomarker could help prioritise th...

Modalities and algorithms for generalized motor seizure detection and prediction: a scoping review.

INTRODUCTION: Generalized tonic - clonic seizures (GTCS) are among the most severe seizure types and a major cause of sudden unexpected death in epile...

A hybrid graph attention network with multi-dimensional features for enhanced EEG-based emotion recognition.

Emotion recognition using electroencephalogram (EEG) signals is a growing focus in affective computing due to its wide-ranging applications in human-c...

CoSuBio: Confidence and success dataset based on multimodal biosignals.

Understanding how self-confidence fluctuates during cognitive activity and how these fluctuations relate to objective physiological signals remains a ...

Dexpression recognition from EEG based on nonlinear analysis and adaptive feature fusion.

Machine learning techniques have recently shown significant promise in electroencephalograph (EEG)-based depression recognition. However, existing met...

Comparison of preprocessing techniques for effective cognitive analysis using electroencephalography (EEG).

Electroencephalography (EEG) serves as a significant technique to analyze the cognition. The purpose of this study is to compare EEG preprocessing tec...

An Enthalpy-Entropy Compensated Ionogel With a Broadband Viscoelastic Plateau for Non-Invasive and High-Fidelity Neurointerfaces.

Achieving non-invasive and high-fidelity electrophysiological recording, particularly electroencephalography (EEG), on dynamic and irregular human ski...

A new graph-transformer framework for EEG-based differentiation of Alzheimer's disease and frontotemporal dementia.

Differentiating between Alzheimer's disease (AD), frontotemporal dementia (FTD), and cognitively normal (CN) subjects remains a significant challenge ...

A Multimodal Dataset for Neurophysiological and AI Applications.

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivit...

Recognizing EEG responses to active TMS vs. sham stimulations in different TMS-EEG datasets: A machine learning approach.

Transcranial Magnetic Stimulation with simultaneous Electroencephalogram (TMS-EEG) allows for the assessment of neurophysiological properties of corti...

Postmarketing Safety of Transcranial Magnetic Stimulation: A 10-Year MAUDE Database Analysis of Adverse Events and Technological Advancements.

Background: Transcranial magnetic stimulation (TMS) is an FDA-cleared neuromodulation technique with expanding clinical applications beyond major depr...

NeuroCLIP: A Multimodal Contrastive Learning Method for rTMS-treated Methamphetamine Addiction Analysis.

Methamphetamine dependence poses a significant global health challenge, yet its assessment and the evaluation of treatments like repetitive transcrani...

Subject-Adaptive EEG Decoding via Filter-Bank Neural Architecture Search for BCI Applications.

Individual differences pose a significant challenge in brain-computer interface (BCI) research. Designing a universally applicable network architectur...

Physician Perspectives on Web-Based Real-World Statistics for Better-Informed Drug Selection in Epilepsy: Mixed Methods Study.

BACKGROUND: Lately, big data studies have shown promise in using patient characteristics to rank the likelihood of retention of antiseizure medication...

Deep Learning-Based Epileptic Seizure Detection from EEG Signals and PPG signals Using LSTM and CNN Models.

Epilepsy is a chronic neurological disorder characterized by recurrent and unpredictable seizures that significantly affect patients' health and quali...

Digitized Canine Olfaction and Multimodal Biosensing for Breath-Based Multi-Cancer Detection: A Hypothesis-Driven Perspective.

Multi-Cancer Early Detection (MCED) is critical for reducing cancer mortality, however current screening technologies have limitations in accessibilit...

EEG foundation models: A critical review of current progress and future directions.

PREMISE: Patterns of electrical brain activity recorded via electroencephalography (EEG) offer immense value for scientific and clinical investigation...

A Comparative Evaluation of 7T MRI for Epilepsy with Deep-Learning-Based Image Reconstruction and Dynamic Parallel Transmission.

OBJECTIVES: 7T MRI enhances lesion detection in epilepsy but is limited by radiofrequency transmission field (B1+) inhomogeneity and long scan times. ...

Lesion network mapping in pediatric epilepsy.

OBJECTIVE: To describe the current use, limitations, and future directions of lesion network mapping in pediatric epilepsy. METHODS: Narrative review ...