Neurology

Seizures

Latest AI and machine learning research in seizures for healthcare professionals.

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A Bimodal Deep Learning Architecture for EEG-fNIRS Decoding of Overt and Imagined Speech.

OBJECTIVE: Brain-computer interfaces (BCI) studies are increasingly leveraging different attributes ...

Spatial-frequency-temporal convolutional recurrent network for olfactory-enhanced EEG emotion recognition.

BACKGROUND: Multimedia stimulation of brain activity is important for emotion induction. Based on br...

Deep-learning-based seizure detection and prediction from electroencephalography signals.

Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The...

A neuroscience-inspired spiking neural network forĀ EEG-based auditory spatial attention detection.

Recent studies have shown that alpha oscillations (8-13 Hz) enable the decoding of auditory spatial ...

An MVMD-CCA Recognition Algorithm in SSVEP-Based BCI and Its Application in Robot Control.

This article proposes a novel recognition algorithm for the steady-state visual evoked potentials (S...

Inference of Brain States Under Anesthesia With Meta Learning Based Deep Learning Models.

Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings an...

Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal.

Detection of mental disorders such as schizophrenia (SZ) through investigating brain activities reco...

Stress Classification Using Brain Signals Based on LSTM Network.

The early diagnosis of stress symptoms is essential for preventing various mental disorder such as d...

Task-State EEG Signal Classification for Spatial Cognitive Evaluation Based on Multiscale High-Density Convolutional Neural Network.

In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method...

Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning.

OBJECTIVE: Most cardiac arrest patients who are successfully resuscitated are initially comatose due...

SCC-MPGCN: self-attention coherence clustering based on multi-pooling graph convolutional network for EEG emotion recognition.

The emotion recognition with electroencephalography (EEG) has been widely studied using the deep lea...

Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training.

Emotion is an important expression generated by human beings to external stimuli in the process of i...

Time-Frequency Analysis of Scalp EEG With Hilbert-Huang Transform and Deep Learning.

Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience a...

Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM).

Emotions are an essential part of daily human communication. The emotional states and dynamics of th...

Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context.

Industrial settings will be characterized by far-reaching production automation brought about by adv...

Deep Learning Enabled Diagnosis of Children's ADHD Based on the Big Data of Video Screen Long-Range EEG.

Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children....

Deep Learning Methods for Multi-Channel EEG-Based Emotion Recognition.

Currently, Fourier-based, wavelet-based, and Hilbert-based time-frequency techniques have generated ...

Deep learning resting state functional magnetic resonance imaging lateralization of temporal lobe epilepsy.

OBJECTIVE: Localization of focal epilepsy is critical for surgical treatment of refractory seizures....

Engineering nonlinear epileptic biomarkers using deep learning and Benford's law.

In this study, we designed two deep neural networks to encode 16 features for early seizure detectio...

EEG Feature Extraction and Data Augmentation in Emotion Recognition.

Emotion recognition is a challenging problem in Brain-Computer Interaction (BCI). Electroencephalogr...

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