O-IPCAC and its Application
to EEG Classification
In this paper we describe an online/incremental linear binary classier based on an inter- esting approach to estimate the Fisher subspace. The proposed method allows to deal with datasets having high cardinality, being dynamically supplied, and it eciently copes with high dimensional data without employing any dimensionality reduction technique. More- over, this approach obtains promising classication performance even when the cardinality of the training set is comparable to the data dimensionality. We demonstrate the ecacy of our algorithm by testing it on EEG data. This classi- cation problem is particularly hard since the data are high dimensional, the cardinality of the data is lower than the space dimensionality, and the classes are strongly unbalanced. The promising results obtained in the MLSP competition, without employing any feature extraction/selection step, have demonstrated that our method is eective; this is further proved both by our tests and by the comparison with other well-known classiers. Keywords: Fisher subspace, Online learning, EEG classication.