Seizures

EEG-Deformer: A Dense Convolutional Transformer for Brain-Computer Interfaces.

Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although Transformers are popular for their long-term sequential learning ability in the BCI field, most methods combining Transformers with convolutional neural networks (CNNs) fail to capture the coarse-to-fine tempora...

Real-Time Epileptic Seizure Prediction Method With Spatio-Temporal Information Transfer Learning.

Despite numerous studies aimed at improving accuracy, the accurate prediction of epileptic seizures remains a challenge in clinical practice due to the high computational cost, poor real-time performance, and over-reliance on labelled data. To address these issues, a real-time seizure prediction method with spatio-temporal information transfer learning (RTSPM-STITL) has been proposed in this study...

On-Chip Mental Stress Detection: Integrating a Wearable Behind-The-Ear EEG Device With Embedded Tiny Neural Network.

The study introduces an innovative approach to efficient mental stress detection by combining electroencephalography (EEG) analysis with on-chip neura...

Near-lossless EEG signal compression using a convolutional autoencoder: Case study for 256-channel binocular rivalry dataset.

Electroencephalography (EEG) experiments typically generate vast amounts of data due to the high sampling rates and the use of multiple electrodes to ...

Canine EEG helps human: cross-species and cross-modality epileptic seizure detection via multi-space alignment.

Epilepsy significantly impacts global health, affecting about 65 million people worldwide, along with various animal species. The diagnostic processes...

Unlocking new frontiers in epilepsy through AI: From seizure prediction to personalized medicine.

Artificial intelligence (AI) is revolutionizing epilepsy care by advancing seizure detection, enhancing diagnostic precision, and enabling personalize...

An Efficient Approach for Detection of Various Epileptic Waves Having Diverse Forms in Long Term EEG Based on Deep Learning.

EEG is the most powerful tool for epilepsy discharge detection in brain. Visual evaluation is hard in long term monitoring EEG data as huge amount of ...

SeizyML: An Application for Semi-Automated Seizure Detection Using Interpretable Machine Learning Models.

Despite the vast number of publications reporting seizures and the reliance of the field on accurate seizure detection, there is a lack of open-source...

A hybrid network based on multi-scale convolutional neural network and bidirectional gated recurrent unit for EEG denoising.

Electroencephalogram (EEG) signals are time series data containing abundant brain information. However, EEG frequently contains various artifacts, suc...

Machine learning analysis of cortical activity in visual associative learning tasks with differing stimulus complexity.

Associative learning tests are cognitive assessments that evaluate the ability of individuals to learn and remember relationships between pairs of sti...

Of Pilots and Copilots: The Evolving Role of Artificial Intelligence in Clinical Neurophysiology.

Artificial intelligence (AI) is revolutionizing clinical neurophysiology (CNP), particularly in its applications to electroencephalography (EEG), elec...

Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification.

Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (M...

Inductive reasoning with large language models: A simulated randomized controlled trial for epilepsy.

INTRODUCTION: To investigate the potential of using artificial intelligence (AI), specifically large language models (LLMs), for synthesizing informat...

Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis.

Neurological disorders are a major global health concern that have a substantial impact on death rates and quality of life. accurately identifying a n...

Epilepsy surgery candidate identification with artificial intelligence: An implementation study.

BACKGROUND: To (a) evaluate the effect of a machine learning algorithm in the identification of patients suitable for epilepsy surgery evaluation, and...

Unsupervised learning from EEG data for epilepsy: A systematic literature review.

BACKGROUND AND OBJECTIVES: Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, whose neurophysiological signature is al...

Machine learning based seizure classification and digital biosignal analysis of ECT seizures.

While artificial intelligence has received considerable attention in various medical fields, its application in the field of electroconvulsive therapy...

Integrating manual preprocessing with automated feature extraction for improved rodent seizure classification.

HYPOTHESIS/OBJECTIVE: Rodent models of epilepsy can help with the search for more effective drug candidates or neuromodulatory therapies. Yet, preclin...

Machine learning classification of active viewing of pain and non-pain images using EEG does not exceed chance in external validation samples.

Previous research has demonstrated that machine learning (ML) could not effectively decode passive observation of neutral versus pain photographs by u...

Predicting Treatment Response of Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder Using an Explainable Machine Learning Model Based on Electroencephalography and Clinical Features.

Major depressive disorder (MDD) is highly heterogeneous in response to repetitive transcranial magnetic stimulation (rTMS), and identifying predictive...