eeg-1

EEG

3260 papers • 126 benchmarks • 313 datasets

Electroencephalography

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in eeg-1

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find eeg-1 models and implementations

vlawhern/arl-eegmodels

3 papers 1,066

Datasets

SPaRCNet

Subtasks

No subtasks available.

Most implemented papers

SGDR: Stochastic Gradient Descent with Warm Restarts

F. Hutter, I. Loshchilov•Fri Aug 12 2016

This paper proposes a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks and empirically studies its performance on the CIFAR-10 and CIFARS datasets.

9751

Content

braindecode/braindecode

3 papers 698

kylemath/DeepEEG

3 papers 256

miaozhengqing/lmda-code

3 papers 49

GeometricBCI/Tensor-CSPNet-and-Grap…

3 papers 44

mcd4874/neurips_competition

3 papers 23

acrophase/sleep_staging_kd

3 papers 14

miracle-2001/gnn4eeg

2 papers 38

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Paper Graph

Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

N. Codella, P. Bashivan, I. Rish, M. Yeasin•Wed Nov 18 2015

This work transforms EEG activities into a sequence of topology-preserving multi-spectral images, as opposed to standard EEG analysis techniques that ignore such spatial information, and trains a deep recurrent-convolutional network inspired by state-of-the-art video classification to learn robust representations from the sequence of images.

707 0

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DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG

Yike Guo, Akara Supratak, Hao Dong, Chao Wu•Sat Mar 11 2017

This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG, and utilizes convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs.

1119 0

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Supplementary for: Deep learning with convolutional neural networks for EEG decoding and visualization

Katharina Eggensperger, F. Hutter, Wolfram Burgard, R. Schirrmeister, Jost Tobias Springenberg, L. Fiederer, M. Glasstetter, M. Tangermann, T. Ball•Sat Dec 31 2016

This research presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and therefore expensive and therefore expensive and therefore time-heavy and expensive process of designing and implementing neural networks.

2993 0

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

C. Igel, Mathias Perslev, M. Jensen, S. Darkner, P. Jennum•Wed Oct 23 2019

U-Time is a temporal fully convolutional network based on the U-Net architecture that was originally proposed for image segmentation developed for the analysis of sleep data and reaches or outperforms current state-of-the-art deep learning models while being much more robust in the training process and without requiring architecture or hyperparameter adaptation across tasks.

305 0

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

L. Benini, Francesco Conti, R. Schilling, Pasquale Davide Schiavone, A. Pullini, D. Rossi, Frank K. Gürkaynak, M. Muehlberghuber, Michael Gautschi, Igor Loi, Germain Haugou, S. Mangard•Sat Dec 17 2016

Fulmine, a system-on-chip (SoC) based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks is proposed.

120 0

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

B. Scholkopf, Niklas Pfister, S. Weichwald, P. Bühlmann•Sun Jun 03 2018

This work introduces 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.

17 0

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

E. Chatzilari, S. Nikolopoulos, Y. Kompatsiaris, V. Oikonomou, G. Liaros, Kostantinos Georgiadis, Katerina Adam•Mon Feb 01 2016

A state-of-the-art baseline for SSVEP-based BCIs is made available for the community that can be used as a basis for introducing novel methods and approaches for brain-computer interfaces.

59 0

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Deep learning-based electroencephalography analysis: a systematic review

Yannick Roy, Hubert J. Banville, Isabela Albuquerque, Alexandre Gramfort, T. Falk, J. Faubert•Tue Jan 15 2019

This work reviews 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain–computer interfacing, and cognitive and affective monitoring, to extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.

1096 0

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