AIMC Topic: Electroencephalography

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Sleep staging classification based on a new parallel fusion method of multiple sources signals.

Physiological measurement Apr 28, 2022
In the field of medical informatics, sleep staging is a challenging and time consuming task undertaken by sleep experts. The conventional method for sleep staging is to analyze Polysomnograms (PSGs) recorded in a sleep lab, but the sleep monitoring w...
Electroencephalography Sleep Algorithms Support Vector Machine Electrooculography Sleep Stages Humans
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Stress Classification Using Brain Signals Based on LSTM Network.

Computational intelligence and neuroscience Apr 28, 2022
The early diagnosis of stress symptoms is essential for preventing various mental disorder such as depression. Electroencephalography (EEG) signals are frequently employed in stress detection research and are both inexpensive and noninvasive modality...
Electroencephalography Electrodes Humans Motion Pictures Neural Networks, Computer Brain
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Task-State EEG Signal Classification for Spatial Cognitive Evaluation Based on Multiscale High-Density Convolutional Neural Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Apr 25, 2022
In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was proposed, aiming at achieving the binary classification of task-state EEG signals before and after spati...
Humans Neural Networks, Computer Electroencephalography Algorithms Cognition
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Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning.

IEEE transactions on bio-medical engineering Apr 21, 2022
OBJECTIVE: Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches large...
Coma Prospective Studies Deep Learning Electroencephalography Humans Heart Arrest
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SCC-MPGCN: self-attention coherence clustering based on multi-pooling graph convolutional network for EEG emotion recognition.

Journal of neural engineering Apr 21, 2022
The emotion recognition with electroencephalography (EEG) has been widely studied using the deep learning methods, but the topology of EEG channels is rarely exploited completely. In this paper, we propose a self-attention coherence clustering based ...
Neural Networks, Computer Cluster Analysis Attention Emotions Electroencephalography
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Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training.

Computational intelligence and neuroscience Apr 15, 2022
Emotion is an important expression generated by human beings to external stimuli in the process of interaction with the external environment. It affects all aspects of our lives all the time. Accurate identification of human emotional states and furt...
Electroencephalography Signal Processing, Computer-Assisted Artificial Intelligence Algorithms Humans Athletes Brain-Computer Interfaces
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Time-Frequency Analysis of Scalp EEG With Hilbert-Huang Transform and Deep Learning.

IEEE journal of biomedical and health informatics Apr 14, 2022
Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience and clinical settings. The conventional EEG analyses usually require pre-defined frequency bands when characterizing neural oscillations and extracting...
Algorithms Deep Learning Humans Brain Waves Scalp Electroencephalography Wavelet Analysis
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Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM).

Sensors (Basel, Switzerland) Apr 13, 2022
Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (EEG) signals that can be used by the Brain-Computer Interface (BCI), to provide better human-machine ...
Deep Learning Emotions Electroencephalography Brain-Computer Interfaces Neural Networks, Computer
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Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context.

Applied ergonomics Apr 8, 2022
Industrial settings will be characterized by far-reaching production automation brought about by advancements in robotics and artificial intelligence. As a consequence, human assembly workers will need to adapt quickly to new and more complex assembl...
Cognition Electroencephalography Electrooculography Artificial Intelligence Eye Movements Humans
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Deep Learning Enabled Diagnosis of Children's ADHD Based on the Big Data of Video Screen Long-Range EEG.

Journal of healthcare engineering Apr 4, 2022
Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children. At the same time, ADHD is prone to coexist with other mental disorders, so the diagnosis of ADHD in children is very important. Electroencephalogram ...
Electroencephalography Attention Deficit Disorder with Hyperactivity Big Data Child Deep Learning Humans Neural Networks, Computer
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