AIMC Topic: Electroencephalography
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Adaptive Multimodel Knowledge Transfer Matrix Machine for EEG Classification.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
The emerging matrix learning methods have achieved promising performances in electroencephalogram (EEG) classification by exploiting the structural information between the columns or rows of feature matrices. Due to the intersubject variability of EE...
An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in brain state recognition studies, it is still challenging to differentiate brain states via explaina...
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the ...
EEG based functional connectivity in resting and emotional states may identify major depressive disorder using machine learning.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Jun 1, 2024
OBJECTIVE: Disrupted brain network connectivity underlies major depressive disorder (MDD). Altered EEG based Functional connectivity (FC) with Emotional stimuli in major depressive disorder (MDD) in addition to resting state FC may help in improving ...
Automatic detection of epilepsy from EEGs using a temporal convolutional network with a self-attention layer.
Biomedical engineering online
Jun 1, 2024
BACKGROUND: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated ...
A 3D ray traced biological neural network learning model.
Nature communications
Jun 1, 2024
Training large neural networks on big datasets requires significant computational resources and time. Transfer learning reduces training time by pre-training a base model on one dataset and transferring the knowledge to a new model for another datase...
Seizure Detection Based on Lightweight Inverted Residual Attention Network.
International journal of neural systems
May 31, 2024
Timely and accurately seizure detection is of great importance for the diagnosis and treatment of epilepsy patients. Existing seizure detection models are often complex and time-consuming, highlighting the urgent need for lightweight seizure detectio...
Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 31, 2024
Electroencephalogram (EEG) is widely used in basic and clinical neuroscience to explore neural states in various populations, and classifying these EEG recordings is a fundamental challenge. While machine learning shows promising results in classifyi...
Exploring the potential of pretrained CNNs and time-frequency methods for accurate epileptic EEG classification: a comparative study.
Biomedical physics & engineering express
May 30, 2024
Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to eva...
Practical measurements distinguishing physiological and pathological stereoelectroencephalography channels based on high-frequency oscillations in the human brain.
Epilepsia open
May 29, 2024
OBJECTIVE: The present study aimed to identify various distinguishing features for use in the accurate classification of stereoelectroencephalography (SEEG) channels based on high-frequency oscillations (HFOs) inside and outside the epileptogenic zon...
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