https://github.com/zabir-nabil/eeg-rsenet

Motor Imagery EEG Signal Classification Using Random Subspace Ensemble Network
https://github.com/zabir-nabil/eeg-rsenet

biosignal classification eeg eeg-classification eeg-matlab eeg-neural-networks matlab-eeg-classification motor-imagery motor-imagery-eeg neural-net random-subspace-ensemble random-subspace-ensemble-network rse-net signal-classification

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Motor Imagery EEG Signal Classification Using Random Subspace Ensemble Network

• Host: GitHub
• URL: https://github.com/zabir-nabil/eeg-rsenet
• Owner: zabir-nabil
• License: mit
• Created: 2018-08-29T15:53:16.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2023-04-30T09:19:56.000Z (over 2 years ago)
• Last Synced: 2025-04-03T02:04:38.541Z (6 months ago)
• Topics: biosignal, classification, eeg, eeg-classification, eeg-matlab, eeg-neural-networks, matlab-eeg-classification, motor-imagery, motor-imagery-eeg, neural-net, random-subspace-ensemble, random-subspace-ensemble-network, rse-net, signal-classification
• Language: Jupyter Notebook
• Homepage:
• Size: 12.8 MB
• Stars: 40
• Watchers: 1
• Forks: 13
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

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

*Running Matlab Scripts:* https://youtu.be/AeRMO98-URc

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

1. Matlab-based baseline for EEG classification. `(minimal matlab)`

2. Classification of motor imagery EEG signals with multi-input convolutional neural network by augmenting STFT. [[IEEE]](https://ieeexplore.ieee.org/document/8975578) [[PDF]](https://www.researchgate.net/publication/335241301_Classification_of_Motor_Imagery_EEG_Signals_with_multi-input_Convolutional_Neural_Network_by_augmenting_STFT) *Notebooks:* `(STFT_CNN_benchmark.ipynb, bci_4_tl_sub1.ipynb, bci_4_tl_sub2.ipynb)`

3. Motor Imagery EEG Classification Using Random Subspace Ensemble Network with Variable Length Features. [[Google Scholar]](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=Fd1-G4YAAAAJ&citation_for_view=Fd1-G4YAAAAJ:eQOLeE2rZwMC) [[PDF]](https://www.researchgate.net/publication/350403311_Motor_Imagery_EEG_Classification_Using_Random_Subspace_Ensemble_Network_with_Variable_Length_Features) *Notebooks:* `(result.ipynb, result_all.ipynb, result_rsenet.ipynb, result_rsenet_all.ipynb, visualization.ipynb)`