Motor Imagery EEG Classification Using Random Subspace Ensemble Network with Variable Length Features

This is a codebase for motor imagery EEG experiments for three projects.

Matlab-based baseline for EEG classification. (minimal matlab)
Classification of motor imagery EEG signals with multi-input convolutional neural network by augmenting STFT. [IEEE] [PDF] Notebooks: (STFT_CNN_benchmark.ipynb, bci_4_tl_sub1.ipynb, bci_4_tl_sub2.ipynb)
Motor Imagery EEG Classification Using Random Subspace Ensemble Network with Variable Length Features. [Google Scholar] [PDF] Notebooks: (result.ipynb, result_all.ipynb, result_rsenet.ipynb, result_rsenet_all.ipynb, visualization.ipynb)

Name		Name	Last commit message	Last commit date
Latest commit History 22 Commits
minimal matlab		minimal matlab
INFO.txt		INFO.txt
LICENSE		LICENSE
README.md		README.md
STFT_CNN_benchmark.ipynb		STFT_CNN_benchmark.ipynb
bci_4_tl_sub1.ipynb		bci_4_tl_sub1.ipynb
bci_4_tl_sub2.ipynb		bci_4_tl_sub2.ipynb
class_distribution.txt		class_distribution.txt
dtcwt_heat.m		dtcwt_heat.m
dwt_denoise.m		dwt_denoise.m
requirements.txt		requirements.txt
result.ipynb		result.ipynb
result_all.ipynb		result_all.ipynb
result_rsenet.ipynb		result_rsenet.ipynb
result_rsenet_all.ipynb		result_rsenet_all.ipynb
rse_net.py		rse_net.py
running_scripts.txt		running_scripts.txt
visualization.ipynb		visualization.ipynb
wavelet_entropy.py		wavelet_entropy.py

zabir-nabil/eeg-rsenet