AIMC Topic: Electroencephalography

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

Scientific reports Jan 2, 2024
Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that appear during sleep. Large-scale sleep studies would benefit from precise and robust automatic sleep event detectors, capable of adapting the variabili...
Deep Learning Humans Electroencephalography Brain Polysomnography Sleep Stages Sleep
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Classification of attention deficit hyperactivity disorder using machine learning on an EEG dataset.

Applied neuropsychology. Child Jan 1, 2024
The neurodevelopmental disorder, Attention Deficit Hyperactivity Disorder (ADHD), frequently affecting youngsters, is characterized by persistent patterns of inattention, hyperactivity, and impulsivity, the etiology of which may involve a variety of ...
Algorithms Female Humans Machine Learning Male Electroencephalography Child Adolescent Attention Deficit Disorder with Hyperactivity
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An emotion recognition method based on EWT-3D-CNN-BiLSTM-GRU-AT model.

Computers in biology and medicine Jan 1, 2024
This has become a significant study area in recent years because of its use in brain-machine interaction (BMI). The robustness problem of emotion classification is one of the most basic approaches for improving the quality of emotion recognition syst...
Artificial Intelligence Emotions Electroencephalography Humans Neural Networks, Computer Attention Wavelet Analysis
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Amplifying pathological detection in EEG signaling pathways through cross-dataset transfer learning.

Computers in biology and medicine Dec 30, 2023
Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders. With the advancement of artificial intelligence methods and machine learning techniques, the potential for accurate...
Machine Learning Electroencephalography Humans Algorithms Artificial Intelligence
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EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification.

Computers in biology and medicine Dec 27, 2023
Because of the intricate topological structure and connection of the human brain, extracting deep spatial features from electroencephalograph (EEG) signals is a challenging and time-consuming task. The extraction of topological spatial information pl...
Humans Electroencephalography Neural Networks, Computer Electrodes Brain
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Brain Topology Modeling With EEG-Graphs for Auditory Spatial Attention Detection.

IEEE transactions on bio-medical engineering Dec 22, 2023
OBJECTIVE: Despite recent advances, the decoding of auditory attention from brain signals remains a challenge. A key solution is the extraction of discriminative features from high-dimensional data, such as multi-channel electroencephalography (EEG)....
Electroencephalography Brain-Computer Interfaces Algorithms Brain Humans Neural Networks, Computer
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Bilateral upper limb robot-assisted rehabilitation improves upper limb motor function in stroke patients: a study based on quantitative EEG.

European journal of medical research Dec 19, 2023
BACKGROUND: Upper limb dysfunction after stroke seriously affects quality of life. Bilateral training has proven helpful in recovery of upper limb motor function in these patients. However, studies evaluating the effectiveness of bilateral upper limb...
Robotics Quality of Life Electroencephalography Activities of Daily Living Treatment Outcome Humans Stroke Rehabilitation Stroke Upper Extremity
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Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals.

Sensors (Basel, Switzerland) Dec 16, 2023
Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor functio...
Signal Processing, Computer-Assisted Humans Artificial Intelligence Electroencephalography Epilepsy Algorithms Seizures Quality of Life
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Electrophysiological brain imaging based on simulation-driven deep learning in the context of epilepsy.

NeuroImage Dec 15, 2023
Identifying the location, the spatial extent and the electrical activity of distributed brain sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the under...
Brain Mapping Electroencephalography Epilepsy Brain Drug Resistant Epilepsy Humans Deep Learning
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An overview of machine learning and deep learning techniques for predicting epileptic seizures.

Journal of integrative bioinformatics Dec 15, 2023
Epilepsy is a neurological disorder (the third most common, following stroke and migraines). A key aspect of its diagnosis is the presence of seizures that occur without a known cause and the potential for new seizures to occur. Machine learning has ...
Epilepsy Seizures Deep Learning Electroencephalography Machine Learning Algorithms Humans
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