AIMC Topic: Electroencephalography

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Identification of Children at Risk of Schizophrenia via Deep Learning and EEG Responses.

IEEE journal of biomedical and health informatics Jan 5, 2021
The prospective identification of children likely to develop schizophrenia is a vital tool to support early interventions that can mitigate the risk of progression to clinical psychosis. Electroencephalographic (EEG) patterns from brain activity and ...
Neural Networks, Computer Prospective Studies Deep Learning Child Electroencephalography Schizophrenia Humans
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Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD Matrices.

IEEE transactions on neural networks and learning systems Jan 4, 2021
Learning vector quantization (LVQ) is a simple and efficient classification method, enjoying great popularity. However, in many classification scenarios, such as electroencephalogram (EEG) classification, the input features are represented by symmetr...
Machine Learning Classification Image Processing, Computer-Assisted Humans Movement Electroencephalography Algorithms Neural Networks, Computer Imagination Cues Reproducibility of Results
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Feature extraction of EEG signals based on functional data analysis and its application to recognition of driver fatigue state.

Physiological measurement Jan 1, 2021
OBJECTIVE: Our objective is to study how to obtain features which can reflect the continuity and internal dynamic changes of electroencephalography (EEG) signals and study an effective method for fatigued driving state recognition based on the obtain...
Humans Data Analysis Algorithms Automobile Driving Support Vector Machine Fatigue Electroencephalography
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A community effort for automatic detection of postictal generalized EEG suppression in epilepsy.

BMC medical informatics and decision making Dec 24, 2020
Applying machine learning to healthcare sheds light on evidence-based decision making and has shown promises to improve healthcare by combining clinical knowledge and biomedical data. However, medicine and data science are not synchronized. Oftentime...
Machine Learning Death, Sudden Humans Electroencephalography Artificial Intelligence Epilepsy
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Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network.

Neural networks : the official journal of the International Neural Network Society Dec 23, 2020
In recent years, deep learning has emerged as a powerful tool for developing Brain-Computer Interface (BCI) systems. However, for deep learning models trained entirely on the data from a specific individual, the performance increase has only been mar...
Imagination Female Transfer, Psychology Psychomotor Performance Young Adult Humans Machine Learning Male Brain-Computer Interfaces Adult Neural Networks, Computer Algorithms Electroencephalography Brain Hand
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Ballistocardiogram Artifact Reduction in Simultaneous EEG-fMRI Using Deep Learning.

IEEE transactions on bio-medical engineering Dec 21, 2020
OBJECTIVE: The concurrent recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a technique that has received much attention due to its potential for combined high temporal and spatial resolution. However, the ...
Algorithms Deep Learning Magnetic Resonance Imaging Humans Electroencephalography Artifacts Ballistocardiography
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Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture.

Sensors (Basel, Switzerland) Dec 21, 2020
Mindfulness training is associated with improvements in psychological wellbeing and cognition, yet the specific underlying neurophysiological mechanisms underpinning these changes are uncertain. This study uses a novel brain-inspired artificial neura...
Brain Mapping Electroencephalography Mindfulness Middle Aged Humans Young Adult Spatio-Temporal Analysis Neural Networks, Computer Adult Brain Male Female
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InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection.

Sensors (Basel, Switzerland) Dec 17, 2020
Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fa...
Electroencephalography Automobile Driving Fatigue Machine Learning Support Vector Machine Humans Neural Networks, Computer
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EEG-Based Emotion Classification for Alzheimer's Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models.

Sensors (Basel, Switzerland) Dec 16, 2020
As the number of patients with Alzheimer's disease (AD) increases, the effort needed to care for these patients increases as well. At the same time, advances in information and sensor technologies have reduced caring costs, providing a potential path...
Alzheimer Disease Humans Neural Networks, Computer Machine Learning Female Emotions Electroencephalography
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Insights on the role of external globus pallidus in controlling absence seizures.

Neural networks : the official journal of the International Neural Network Society Dec 14, 2020
Absence epilepsy, characterized by transient loss of awareness and bilaterally synchronous 2-4 Hz spike and wave discharges (SWDs) on electroencephalography (EEG) during absence seizures, is generally believed to arise from abnormal interactions betw...
Humans Neural Networks, Computer Electroencephalography Seizures Neurons Cerebral Cortex Globus Pallidus Neural Pathways Thalamus
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