MATLAB scripts to reproduce the results of our NIPS 2006 paper
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README.md

README.md

Matlab code for "Logistic Regression for Single Trial EEG Classification" (NIPS 2006)

Instructions

Please unzip the attachment. The main solver routine is called lrr2.m You should preprocess each epoch into convariance matrix and apply whitening before calling lrr2. Basically lrr2 can be called as

   [W, bias, stat] = lrr2(Xtr, Ytr, lambda);

where Xtr is CxCxn (C: #channels, n: #epochs), and Ytr is nx1 (vector of -1 or +1), and lambda is the regularization constant. W is Cx2 coefficient matrix and bias is the bias term.

The program uses the MATLAB optimization toolbox (fminunc). If you don't have the toolbox, you can also choose an internal L-BFGS solver by specifying the 'solver' option as

   [W, bias, stat] = lrr2(Xtr, Ytr, lambda, 'solver','lbfgs');

However, since L-BFGS assumes that the Hessian is possitive definite, the result is not as stable as the optimization toolbox. Note that the objective function is not convex.

To see how the data should be preprocessed and everything, please run the script s_bcicompIIIiva.m. Please also download the BCI Competition III dataset iv to run the script. http://www.bbci.de/competition/iii/index.html#data_set_iva

Notes

  • Feedback and comments are welcome. Email: tomioka [AT] ttic [DOT] edu
  • This software is provided as is and although I will try to help you to make the best use out of it, I will not take any leagal responsibility about the consequence.
  • My limited memory BFGS routine is based on Jorge Nocedal's work and Naoaki Okazaki's liblbfgs.