Benchmarks
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Subtasks
Greatest papers with code
SGDR: Stochastic Gradient Descent with Warm Restarts
• rwightman/pytorch-image-models
• 
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.
An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave
The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases.
ARRHYTHMIA DETECTION EEG SEIZURE DETECTION SLEEP STAGE DETECTION
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.
Decoding P300 Variability using Convolutional Neural Networks
Frontiers in Human Neuroscience 2019
• vlawhern/arl-eegmodels
• 
Deep convolutional neural networks (CNN) have previously been shown to be useful tools for signal decoding and analysis in a variety of complex domains, such as image processing and speech recognition.
Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials
Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli.
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.
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.
Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals
In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes.
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals
To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain motor imagery.