AIMC Topic: Electroencephalography

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A deep learning framework for epileptic seizure detection based on neonatal EEG signals.

Scientific reports Jul 29, 2022
Electroencephalogram (EEG) is one of the main diagnostic tests for epilepsy. The detection of epileptic activity is usually performed by a human expert and is based on finding specific patterns in the multi-channel electroencephalogram. This is a dif...
Signal Processing, Computer-Assisted Infant, Newborn Deep Learning Humans Epilepsy Neural Networks, Computer Seizures Electroencephalography
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SleepFCN: A Fully Convolutional Deep Learning Framework for Sleep Stage Classification Using Single-Channel Electroencephalograms.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jul 29, 2022
Sleep is a vital process of our daily life as we roughly spend one-third of our lives asleep. In order to evaluate sleep quality and potential sleep disorders, sleep stage classification is a gold standard method. In this paper, we introduce a novel ...
Sleep Deep Learning Sleep Stages Electroencephalography Humans Neural Networks, Computer
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Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics.

Proceedings of the National Academy of Sciences of the United States of America Jul 26, 2022
Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning-b...
Humans Magnetic Resonance Imaging Brain Mapping Neural Networks, Computer Brain Electroencephalography
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Sex differences in rTMS treatment response: A deep learning-based EEG investigation.

Brain and behavior Jul 25, 2022
INTRODUCTION: The present study aimed to investigate sex differences in response to repetitive transcranial magnetic stimulation (rTMS) in Major Depressive Disorder (MDD) patients. Identifying the factors that mediate treatment response to rTMS in MD...
Sex Characteristics Depressive Disorder, Major Transcranial Magnetic Stimulation Deep Learning Treatment Outcome Electroencephalography Female Male Humans
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Impact of EEG Frequency Bands and Data Separation on the Performance of Person Verification Employing Neural Networks.

Sensors (Basel, Switzerland) Jul 25, 2022
The paper is devoted to the study of EEG-based people verification. Analyzed solutions employed shallow artificial neural networks using spectral EEG features as input representation. We investigated the impact of the features derived from different ...
Electroencephalography Neural Networks, Computer Adult Humans
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Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users.

PloS one Jul 22, 2022
Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with mental imagination of movement and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine...
Imagination Humans Imagery, Psychotherapy Electroencephalography Algorithms Brain-Computer Interfaces Deep Learning
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Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network.

Computational intelligence and neuroscience Jul 21, 2022
The efficient biological signal processing method can effectively improve the efficiency of researchers to explore the work of life mechanism, so as to better reveal the relationship between physiological structure and function, thus promoting the ge...
Molybdenum Humans Electroencephalography Neural Networks, Computer Signal Processing, Computer-Assisted
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Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework.

Sensors (Basel, Switzerland) Jul 20, 2022
Although increasing evidences support the notion that psychiatric disorders are associated with abnormal communication between brain regions, scattered studies have investigated brain electrophysiological disconnectivity of patients with generalized ...
Entropy Anxiety Disorders Electroencephalography Humans Machine Learning Brain
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A Hybrid Expert System for Individualized Quantification of Electrical Status Epilepticus During Sleep Using Biogeography-Based Optimization.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jul 19, 2022
Electrical status epilepticus during sleep (ESES) is an epileptic encephalopathy in children with complex clinical manifestations. It is accompanied by specific electroencephalography (EEG) patterns of continuous spike and slow-waves. Quantifying suc...
China Child Electroencephalography Sleep Humans Reproducibility of Results Expert Systems Status Epilepticus
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A multi-modal assessment of sleep stages using adaptive Fourier decomposition and machine learning.

Computers in biology and medicine Jul 14, 2022
Healthy sleep is essential for the rejuvenation of the body and helps in maintaining good health. Many people suffer from sleep disorders that are characterized by abnormal sleep patterns. Automated assessment of such disorders using biomedical signa...
Humans Polysomnography Machine Learning Electroencephalography Electrooculography Signal Processing, Computer-Assisted Sleep Stages
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