Electroencephalogram (EEG)

336 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.


Use these libraries to find Electroencephalogram (EEG) models and implementations

Most implemented papers

SGDR: Stochastic Gradient Descent with Warm Restarts

loshchil/SGDR 13 Aug 2016

Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions.

Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

pbashivan/EEGLearn 19 Nov 2015

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data.

EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces

vlawhern/arl-eegmodels 23 Nov 2016

We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI.

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

akaraspt/deepsleepnet 12 Mar 2017

This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different datasets without utilizing any hand-engineered features.

Deep learning with convolutional neural networks for EEG decoding and visualization

braindecode/braindecode 15 Mar 2017

PLEASE READ AND CITE THE REVISED VERSION at Human Brain Mapping: http://onlinelibrary. wiley. com/doi/10. 1002/hbm. 23730/full Code available here: https://github. com/robintibor/braindecode

U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging

perslev/U-Time NeurIPS 2019

We propose U-Time, a fully feed-forward deep learning approach to physiological time series segmentation developed for the analysis of sleep data.

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

pulp-platform/pulp 18 Dec 2016

Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline.

Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs

MAMEM/ssvep-eeg-processing-toolbox 2 Feb 2016

Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities.

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

sweichwald/coroICA-python 4 Jun 2018

We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding.

Deep learning-based electroencephalography analysis: a systematic review

kylemath/DeepEEG 16 Jan 2019

To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.