MI-classification-using-weighted sparsity

Classification of Motor Imagery EEG signal using weighted sparsity approach

Dataset used:

Dataset IVa from BCI competition III http://www.bbci.de/competition/iii.

This project is implemented in python 2.7.

To make easier the installation of Python dependencies, we recommend the Anaconda Python distribution.

We recommend to install jupyter notebook.

In addition, the knowledge base for EEG interpretation depends on the following packages:

scipy
scikit-learn
pywt
wyrm

To support visualization of the interpretation results use matplotlib package.

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README.md		README.md
weighted_Subject aa_dictionary based classification without BP and CSP_final.ipynb		weighted_Subject aa_dictionary based classification without BP and CSP_final.ipynb
weighted_Subject al_dictionary based classification without BP and CSP_final.ipynb		weighted_Subject al_dictionary based classification without BP and CSP_final.ipynb
weighted_Subject av_dictionary based classification without BP and CSP_final.ipynb		weighted_Subject av_dictionary based classification without BP and CSP_final.ipynb
weighted_Subject aw_dictionary based classification without BP and CSP_final.ipynb		weighted_Subject aw_dictionary based classification without BP and CSP_final.ipynb
weighted_Subject ay_dictionary based classification without BP and CSP_final.ipynb		weighted_Subject ay_dictionary based classification without BP and CSP_final.ipynb