Electroencephalogram (EEG)
334 papers with code • 3 benchmarks • 7 datasets
Electroencephalogram (EEG) is a method of recording brain activity using electrophysiological indexes. When the brain is active, a large number of postsynaptic potentials generated synchronously by neurons are formed after summation. It records the changes of electric waves during brain activity and is the overall reflection of the electrophysiological activities of brain nerve cells on the surface of cerebral cortex or scalp. Brain waves originate from the postsynaptic potential of the apical dendrites of pyramidal cells. The formation of synchronous rhythm of EEG is also related to the activity of nonspecific projection system of cortex and thalamus. EEG is the basic theoretical research of brain science. EEG monitoring is widely used in its clinical application.
Libraries
Use these libraries to find Electroencephalogram (EEG) models and implementations
SEED
ICLabel
CWL EEG/fMRI Dataset
Subtasks
Most implemented papers
Cerebral Signal Instantaneous Parameters Estimation MATLAB Toolbox - User Guide Version 2.3
This document is meant to help individuals use the Cerebral Signal Phase Analysis toolbox which implements different methods for estimating the instantaneous phase and frequency of a signal and calculating some related popular quantities. The toolbox -- which is distributed under the terms of the GNU GENERAL PUBLIC LICENSE as a set of MATLAB routines -- can be downloaded at the address http://oset. ir/category. php? dir=Tools. The purpose of this toolbox is to calculate the instantaneous phase and frequency sequences of cerebral signals (EEG, MEG, etc.)
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
robintibor/auto-eeg-diagnosis-example
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We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus.
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
xiangzhang1015/Brain_typing
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An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots.
ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification
Brain-related disorders such as epilepsy can be diagnosed by analyzing electroencephalograms (EEG).
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals
MichaelMurashov/ecg-testing
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Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data.
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features
Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems.
Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG
kylemath/DeepEEG
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We present the first real-time sleep staging system that uses deep learning without the need for servers in a smartphone application for a wearable EEG.
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition.
Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals
xiangzhang1015/EEG_Shape_Reconstruction
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In light of this, we propose a novel multi-task generative adversarial network to convert the individual's EEG signals evoked by geometrical shapes to the original geometry.
Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification
tiehangd/MUPS
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The pattern of Electroencephalogram (EEG) signal differs significantly across different subjects, and poses challenge for EEG classifiers in terms of 1) effectively adapting a learned classifier onto a new subject, 2) retaining knowledge of known subjects after the adaptation.
