zuo785843091 / eeg-rsenet

Motor Imagery EEG Signal Classification Using Random Subspace Ensemble Network

eeg-rsenet

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

email: [email protected]

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

There are motor imagery EEG classification code for 3 projects in this repository.

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. (https://www.researchgate.net/publication/335241301_Classification_of_Motor_Imagery_EEG_Signals_with_multi-input_Convolutional_Neural_Network_by_augmenting_STFT) (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. (https://www.researchgate.net/publication/350403311_Motor_Imagery_EEG_Classification_Using_Random_Subspace_Ensemble_Network_with_Variable_Length_Features)

Citation(s):

Zabir Al Nazi, A. B. M. Aowlad Hossain, Md. Monirul Islam (2021) Motor Imagery EEG Classification Using Random Subspace Ensemble Network with Variable Length Features, Int J Bioautomation, 25 (1), 13-24, doi: 10.7546/ijba.2021.25.1.000611

Shovon, T. H., Al Nazi, Z., Dash, S., & Hossain, M. F. (2019, September). Classification of motor imagery eeg signals with multi-input convolutional neural network by augmenting stft. In 2019 5th International Conference on Advances in Electrical Engineering (ICAEE) (pp. 398-403). IEEE.

BibTex:

http://biomed.bas.bg/bioautomation/2021/vol_25.1/toc.html doi: 10.7546/ijba.2021.25.1.000611

@article{al2021motor,
  title={Motor Imagery EEG Classification Using Random Subspace Ensemble Network with Variable Length Features},
  author={Al Nazi, Zabir and Hossain, ABM Aowlad and Islam, Md Monirul},
  journal={International Journal Bioautomation},
  volume={25},
  number={1},
  pages={13},
  year={2021},
  publisher={Bulgarska Akademiya na Naukite/Bulgarian Academy of Sciences}
}

@inproceedings{shovon2019classification,
  title={Classification of motor imagery eeg signals with multi-input convolutional neural network by augmenting stft},
  author={Shovon, Tanvir Hasan and Al Nazi, Zabir and Dash, Shovon and Hossain, Md Foisal},
  booktitle={2019 5th International Conference on Advances in Electrical Engineering (ICAEE)},
  pages={398--403},
  year={2019},
  organization={IEEE}
}