Electroencephalogram (EEG)

329 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

Most implemented papers

SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

SajadMo/SleepEEGNet 5 Mar 2019

Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders.

SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type Classification

IBM/seizure-type-classification-tuh 8 Mar 2019

Automatic classification of epileptic seizure types in electroencephalograms (EEGs) data can enable more precise diagnosis and efficient management of the disease.

Advancing NLP with Cognitive Language Processing Signals

DS3Lab/zuco-nlp 4 Apr 2019

Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks.

Transformer-based Spatial-Temporal Feature Learning for EEG Decoding

anranknight/EEG-Transformer 11 Jun 2021

As far as we know, it is the first time that a detailed and complete method based on the transformer idea has been proposed in this field.

Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework

miaozhengqing/lmda-code 19 Feb 2022

Compared to the vanilla EEGNet and ConvNet, the proposed SDDA framework was able to boost the MI classification accuracy by 15. 2%, 10. 2% respectively in IIA dataset, and 5. 5%, 4. 2% in IIB dataset.

An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave

MIT-LCP/wfdb-python Journal of Open Research Software 2014

The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases.

Using Riemannian geometry for SSVEP-based Brain Computer Interface

emmanuelkalunga/Online-SSVEP 14 Jan 2015

Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results.

Online SSVEP-based BCI using Riemannian geometry

emmanuelkalunga/Online-SSVEP Neurocomputing 2016

We propose a novel algorithm for online and asynchronous processing of brain signals, borrowing principles from semi-unsupervised approaches and following a dynamic stopping scheme to provide a prediction as soon as possible.

Deep Learning Human Mind for Automated Visual Classification

AliAbyaneh/Extracting-Image-from-EEG-signals CVPR 2017

In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories.

Cerebral Signal Instantaneous Parameters Estimation MATLAB Toolbox - User Guide Version 2.3

EsiSeraj/EEG-PhaseFreq-Analysis 7 Oct 2016

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.)