Seizures

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 software tools in the scientific community for automating seizure detection based on electrographic recordings. Researchers instead rely on manual curation of seizure detection that is highly laborious, inefficient and can be error prone and heavily...

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, such as electromyographic, electrooculographic, and electrocardiographic. These artifacts can change EEG waveforms and affect the accuracy and reliability of neuroscientific studies. Recent research has demonstrated that end-to-end deep learning approac...

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...

A novel deep learning model combining 3DCNN-CapsNet and hierarchical attention mechanism for EEG emotion recognition.

Emotion recognition plays a key role in the field of human-computer interaction. Classifying and predicting human emotions using electroencephalogram ...

Explaining electroencephalogram channel and subband sensitivity for alcoholism detection.

Alcoholism, a progressive loss of control over alcohol consumption, deteriorates mental and physical health over time. Automatic alcoholism detection ...

A systematic literature review of machine learning techniques for the detection of attention-deficit/hyperactivity disorder using MRI and/or EEG data.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition common in teenagers across the globe. Neuroimaging and Machine Learn...

Latent alignment in deep learning models for EEG decoding.

. Brain-computer interfaces (BCIs) face a significant challenge due to variability in electroencephalography (EEG) signals across individuals. While r...

Efficient Neural Network Classification of Parkinson's Disease and Schizophrenia Using Resting-State EEG Data.

Timely identification of Parkinson's disease and schizophrenia is crucial for the effective management and enhancement of patients' quality of life. T...

A hybrid optimization-enhanced 1D-ResCNN framework for epileptic spike detection in scalp EEG signals.

In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D residual convolutional neural networks (1D-ResCNN...

A combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features.

Developing a robust and effective technique is crucial for interpreting a user's brainwave signals accurately in the realm of biomedical signal proces...