One significant step in brain-computer interface (BCI) signal processing is feature extraction, in motor-imagery (MI) paradigm a commonly used method is called common-spatial pattern (CSP). This is my implementation of CSP algorithm on BCI dataset IV 2a. The algorithm implemented in this code is based on [1], details of the dataset can be seen on [2]
SVM model with scikit-learn default configuration is used to evaluate training data, then this model is used to evaluate score on test data
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Next, randomized search method is used to tune hyperparameters, the model with best hyperparameters is evaluated on same train and test dataset
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[1] B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe and K. Muller, "Optimizing Spatial filters for Robust EEG Single-Trial Analysis," in IEEE Signal Processing Magazine, vol. 25, no. 1, pp. 41-56, 2008, doi: 10.1109/MSP.2008.4408441.
[2] C. Brunner, R. Leeb, G. Mller-Putz, A. Schlögl and G. Pfurtscheller, “BCI Competition 2008 Graz Data Set a”, 2008.
- develop folder contains previous version of CSP code, might be deleted later
- the code is still in progress, need to work on how to evaluate on test dataset
- BCICIVC2a stands for BCI Competition IV dataset 2a