Seizures

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-computer interaction. However, many existing studies process EEG signals as independent one-dimensional time series, overlooking its multidimensional structure and dynamic segment relationships. To address this, we propose a novel Hybrid Graph Attenti...

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 challenge in psychological and computational research. Existing multimodal datasets predominantly focus on stress, affect, or workload, and do not systematically capture experimentally manipulated self-confidence states. The CoSuBio dataset addresses...

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 ...

Exploring the potential of explainable deep learning for EEG-based cognitive decline prediction.

OBJECTIVE: Detecting Alzheimer's disease (AD) at an early stage is essential for administering effective treatments and preventing neuronal damage. Un...

Memristive Reservoir Computing Circuit for Real-Time Prediction of Epilepsy.

The prediction of epileptic seizures can significantly improve patients' quality of life by enabling timely preventive interventions. However, realizi...

TPCNet: A Temporal Periodicity Convolutional Network for motor imagery EEG decoding in stroke patients.

BACKGROUND: Stroke caused by vascular rupture or blockage has high incidence and leads to significant disability. Motor imagery (MI) electroencephalog...