AIMC Topic: Electroencephalography

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Social anxiety prediction based on ERP features: A deep learning approach.

Journal of affective disorders Sep 3, 2024
BACKGROUND: Social Anxiety Disorder is traditionally diagnosed using subjective scales that may lack accuracy. Recently, EEG technology has gained importance for anxiety detection due to its ability to capture stable and objective neurophysiological ...
Humans Female Young Adult Anxiety Deep Learning Electroencephalography Adult Male Evoked Potentials Facial Recognition Phobia, Social Facial Expression
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Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks.

Journal of neuroscience methods Sep 3, 2024
BACKGROUND: Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging due to physiological differences between subjects. Domain gap, which refers to the different distribut...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Humans Neural Networks, Computer Emotions Electroencephalography Brain
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Brain Emotion Perception Inspired EEG Emotion Recognition With Deep Reinforcement Learning.

IEEE transactions on neural networks and learning systems Sep 3, 2024
Inspired by the well-known Papez circuit theory and neuroscience knowledge of reinforcement learning, a double dueling deep Q network (DQN) is built incorporating the electroencephalogram (EEG) signals of the frontal lobe as prior information, which ...
Neural Networks, Computer Frontal Lobe Brain Perception Emotions Reinforcement, Psychology Deep Learning Electroencephalography Humans
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SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification.

IEEE transactions on neural networks and learning systems Sep 3, 2024
In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The goal is to develop a lightweighted deep learning model while retaining a high level of classificati...
Humans Neural Networks, Computer Algorithms Electroencephalography Epilepsy Deep Learning Signal Processing, Computer-Assisted
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SleepGCN: A transition rule learning model based on Graph Convolutional Network for sleep staging.

Computer methods and programs in biomedicine Sep 2, 2024
BACKGROUND AND OBJECTIVE: Automatic sleep staging is essential for assessing and diagnosing sleep disorders, serving millions of people who suffer from them. Numerous sleep staging models have been proposed recently, but most of them have not fully e...
Signal Processing, Computer-Assisted Electrooculography Sleep Stages Algorithms Electroencephalography Neural Networks, Computer Machine Learning Humans
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Artificial intelligence and telemedicine in epilepsy and EEG: A narrative review.

Seizure Aug 30, 2024
The emergence of telemedicine and artificial intelligence (AI) has set the stage for a possible revolution in the future of medicine and neurology including the diagnosis and management of epilepsy. Telemedicine, with its proven efficacy during the C...
Electroencephalography Humans Epilepsy Machine Learning Telemedicine Artificial Intelligence COVID-19
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CTNet: a convolutional transformer network for EEG-based motor imagery classification.

Scientific reports Aug 30, 2024
Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling the tr...
Brain Electroencephalography Brain-Computer Interfaces Imagination Signal Processing, Computer-Assisted Humans Neural Networks, Computer Algorithms
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Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks.

Neural networks : the official journal of the International Neural Network Society Aug 28, 2024
In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers for specific mental tasks is critical for BCI performance. The classifiers are developed by machine learning (ML) and deep learning (DL) techniques, requiring...
Electroencephalography Brain-Computer Interfaces Machine Learning Humans Imagination Brain Neural Networks, Computer Deep Learning
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Schizophrenia diagnosis using the GRU-layer's alpha-EEG rhythm's dependability.

Psychiatry research. Neuroimaging Aug 28, 2024
Verifying schizophrenia (SZ) can be assisted by deep learning techniques and patterns in brain activity observed in alpha-EEG recordings. The suggested research provides evidence of the reliability of alpha-EEG rhythm in a Gated-Recurrent-Unit-based ...
Schizophrenia Alpha Rhythm Reproducibility of Results Humans Deep Learning Neural Networks, Computer Electroencephalography
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Bio-inspired EEG signal computing using machine learning and fuzzy theory for decision making in future-oriented brain-controlled vehicles.

SLAS technology Aug 28, 2024
One kind of autonomous vehicle that can take instructions from the driver by reading their electroencephalogram (EEG) signals using a Brain-Computer Interface (BCI) is called a Brain-Controlled Vehicle (BCV). The operation of such a vehicle is greatl...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Fuzzy Logic Decision Making Humans Electroencephalography Machine Learning
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