EEG Signal Classification
8 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in EEG Signal Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Gun Detection Dataset
Most implemented papers
A Transformer-based deep neural network model for SSVEP classification
The proposed model validates the feasibility of deep learning models based on Transformer structure for SSVEP classification task, and could serve as a potential model to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems.
Imagined speech classification using EEG
The objective of this work is to assess the possibility of using (Electroencephalogram) EEG for communication between different subjects.
Federated Transfer Learning for EEG Signal Classification
DashanGao/Federated-Transfer-Learning-for-EEG
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The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets.
A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG
cuijiancorbin/A-Compact-and-Interpretable-Convolutional-Neural-Network-for-Single-Channel-EEG
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Driver drowsiness is one of main factors leading to road fatalities and hazards in the transportation industry.
Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interface
We propose a novel approach called time-frequency common spatial patterns (TFCSP) to enhance the robustness and accuracy of the electroencephalogram (EEG) signal classification.
Deep comparisons of Neural Networks from the EEGNet family
In this article, we compared 5 well-known neural networks (Shallow ConvNet, Deep ConvNet, EEGNet, EEGNet Fusion, MI-EEGNet) using open-access databases with many subjects next to the BCI Competition 4 2a dataset to acquire statistically significant results.
Improving EEG Signal Classification Accuracy Using Wasserstein Generative Adversarial Networks
Electroencephalography (EEG) plays a vital role in recording brain activities and is integral to the development of brain-computer interface (BCI) technologies.
EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs
We show that our semantic subsequence preserving improves the existing masking methods in self-prediction literature and find that preserving 50\% of EEG recordings will result in the most accurate results on all 6 tasks on average.
