snailpt/CTNet
CTNet is a convolutional Transformer neural network for classifying motor imagery from EEG brain signals.
Not currently ranked — collecting fresh signals.
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CTNet combines a convolutional module (inspired by EEGNet) for extracting local and spatial features from EEG time series with a Transformer encoder module using multi-head attention to capture global dependencies in high-level features. A fully connected layer classifier then categorizes the EEG signals into motor imagery classes. The model achieved 82.52% accuracy on BCI IV-2a and 88.49% on BCI IV-2b datasets. It has been integrated into the braindecode toolbox.
Frequently asked
- What is snailpt/CTNet?
- CTNet is a convolutional Transformer neural network for classifying motor imagery from EEG brain signals.
- Is CTNet open source?
- Yes — snailpt/CTNet is open source, released under the MIT license.
- What language is CTNet written in?
- snailpt/CTNet is primarily written in Jupyter Notebook.
- How popular is CTNet?
- snailpt/CTNet has 448 stars on GitHub.
- Where can I find CTNet?
- snailpt/CTNet is on GitHub at https://github.com/snailpt/CTNet.