Seizures

The expert's knowledge combined with AI outperforms AI alone in seizure onset zone localization using resting state fMRI.

We evaluated whether integration of expert guidance on seizure onset zone (SOZ) identification from resting state functional MRI (rs-fMRI) connectomics combined with deep learning (DL) techniques enhances the SOZ delineation in patients with refractory epilepsy (RE), compared to utilizing DL alone. Rs-fMRI was collected from 52 children with RE who had subsequently undergone ic-EEG and then, if in...

Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series.

BACKGROUND: Individuals in the early stages of Alzheimer's Disease (AD) are typically diagnosed with Mild Cognitive Impairment (MCI). MCI represents a transitional phase between normal cognitive function and AD. Electroencephalography (EEG) records carry valuable insights into cerebral cortex brain activities to analyze neuronal degeneration. To enhance the precision of dementia diagnosis, automat...

The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization.

For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are ...

Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data.

Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this condition overlap and co-occur with those of other ...

Robotic assessment of sensorimotor and cognitive deficits in patients with temporal lobe epilepsy.

OBJECTIVE: Individuals with temporal lobe epilepsy (TLE) frequently demonstrate impairments in executive function, working memory, and/or declarative ...

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol.

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memor...

Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm.

INTRODUCTION: The main objective of this study is to evaluate working memory and determine EEG biomarkers that can assist in the field of health neuro...

EEG-BCI-based motor imagery classification using double attention convolutional network.

This article aims to improve and diversify signal processing techniques to execute a brain-computer interface (BCI) based on neurological phenomena ob...

A robust deep learning detector for sleep spindles and K-complexes: towards population norms.

Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that appear during sleep. Large-scale sleep studies woul...

Classification of attention deficit hyperactivity disorder using machine learning on an EEG dataset.

The neurodevelopmental disorder, Attention Deficit Hyperactivity Disorder (ADHD), frequently affecting youngsters, is characterized by persistent patt...

An emotion recognition method based on EWT-3D-CNN-BiLSTM-GRU-AT model.

This has become a significant study area in recent years because of its use in brain-machine interaction (BMI). The robustness problem of emotion clas...

Quercetin Exerts Anticonvulsant Effect through Mitigation of Neuroinflammatory Response in Pentylenetetrazole-induced Seizure in Mice.

Epilepsy is a chronic disease of the brain characterized by seizures. The currently available anticonvulsants only treat symptoms with serious adverse...

Amplifying pathological detection in EEG signaling pathways through cross-dataset transfer learning.

Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders. With the advanc...

EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification.

Because of the intricate topological structure and connection of the human brain, extracting deep spatial features from electroencephalograph (EEG) si...

Cognitive workload estimation using physiological measures: a review.

Estimating cognitive workload levels is an emerging research topic in the cognitive neuroscience domain, as participants' performance is highly influe...

Portable Multi-focal Visual Evoked Potential Diagnostics for Multiple Sclerosis/Optic Neuritis patients.

PURPOSE: Multiple Sclerosis (MS) is a neuro-inflammatory disease of the Central Nervous System (CNS) in which the body's immune system attacks and des...

Brain Topology Modeling With EEG-Graphs for Auditory Spatial Attention Detection.

OBJECTIVE: Despite recent advances, the decoding of auditory attention from brain signals remains a challenge. A key solution is the extraction of dis...

Metaphoric language in the differential diagnosis of epilepsy and psychogenic non-epileptic seizures: Time to move forward.

Conversation analysis (CA) to identify metaphoric language (ML) has been proposed as a tool for the differential diagnosis of epileptic (ES) and psych...

Bilateral upper limb robot-assisted rehabilitation improves upper limb motor function in stroke patients: a study based on quantitative EEG.

BACKGROUND: Upper limb dysfunction after stroke seriously affects quality of life. Bilateral training has proven helpful in recovery of upper limb mot...

Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals.

Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient o...