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

Train score	Test score

Next, randomized search method is used to tune hyperparameters, the model with best hyperparameters is evaluated on same train and test dataset

Train score	Test score

[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