EEG
394 papers with code • 0 benchmarks • 1 datasets
Electroencephalography
Benchmarks
These leaderboards are used to track progress in EEG
Libraries
Use these libraries to find EEG models and implementations
SPaRCNet
Most implemented papers
SGDR: Stochastic Gradient Descent with Warm Restarts
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
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
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
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
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
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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
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
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
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
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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.
