AIMC Topic: Electroencephalography

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EEG based depression recognition using improved graph convolutional neural network.

Computers in biology and medicine Jul 8, 2022
Depression is a global psychological disease that does serious harm to people. Traditional diagnostic method of the doctor-patient communication, is not objective and accurate enough. Thus, a more accurate and objective method for depression detectio...
Depression Electroencephalography Attention Neural Networks, Computer Brain Humans
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Granger Causality Inference in EEG Source Connectivity Analysis: A State-Space Approach.

IEEE transactions on neural networks and learning systems Jul 6, 2022
This article addresses the problem of estimating brain effective connectivity from electroencephalogram (EEG) signals using a Granger causality (GC) characterized on state-space models, extended from the conventional vector autoregressive (VAR) proce...
Electroencephalography Humans Computer Simulation Least-Squares Analysis Neural Networks, Computer Brain
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Spatio-Spectral Feature Representation for Motor Imagery Classification Using Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems Jul 6, 2022
Convolutional neural networks (CNNs) have recently been applied to electroencephalogram (EEG)-based brain-computer interfaces (BCIs). EEG is a noninvasive neuroimaging technique, which can be used to decode user intentions. Because the feature space ...
Humans Electroencephalography Neuroimaging Brain-Computer Interfaces Neural Networks, Computer Algorithms
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EEGSym: Overcoming Inter-Subject Variability in Motor Imagery Based BCIs With Deep Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jul 4, 2022
In this study, we present a new Deep Learning (DL) architecture for Motor Imagery (MI) based Brain Computer Interfaces (BCIs) called EEGSym. Our implementation aims to improve previous state-of-the-art performances on MI classification by overcoming ...
Algorithms Humans Brain-Computer Interfaces Deep Learning Neural Networks, Computer Imagination Electroencephalography Hand
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Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm.

Sensors (Basel, Switzerland) Jul 2, 2022
Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to ...
Electroencephalography Brain-Computer Interfaces Software Robotics Evoked Potentials Evoked Potentials, Visual Photic Stimulation Humans
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A Multimodal AI System for Out-of-Distribution Generalization of Seizure Identification.

IEEE journal of biomedical and health informatics Jul 1, 2022
Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many laborious and time-consuming processes in hospitals or ambulatory settings, e.g. home monitoring and telehealth. One such unmet challenge is rapid and acc...
Humans Seizures Electroencephalography Artificial Intelligence Epilepsy Prospective Studies
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SEEG-Net: An explainable and deep learning-based cross-subject pathological activity detection method for drug-resistant epilepsy.

Computers in biology and medicine Jun 29, 2022
OBJECTIVE: Precise preoperative evaluation of drug-resistant epilepsy (DRE) requires accurate analysis of invasive stereoelectroencephalography (SEEG). With the tremendous breakthrough of Artificial intelligence (AI), previous studies can help clinic...
Stereotaxic Techniques Drug Resistant Epilepsy Deep Learning Electroencephalography Humans Artificial Intelligence
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A method for AI assisted human interpretation of neonatal EEG.

Scientific reports Jun 29, 2022
The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method's suitability is assessed through acoustic detection of the presence of neonat...
Epilepsy Child Humans Seizures Electroencephalography Algorithms Artificial Intelligence Infant, Newborn
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Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load.

eLife Jun 29, 2022
Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by reporting isolated sleep rhythms such as slow oscillations and spindles. What is the spatial scale of...
Electrocorticography Animals Deep Learning Sleep Memory Electroencephalography
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Drowsiness Detection Using Ocular Indices from EEG Signal.

Sensors (Basel, Switzerland) Jun 24, 2022
Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. Howeve...
Algorithms Support Vector Machine Humans Machine Learning Electroencephalography Signal Processing, Computer-Assisted
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