A novel statistical algorithm for multiclass EEG signal classification

This paper presents a new algorithm for the classification of multiclass EEG signals. This algorithm involves applying the optimum allocation technique to select representative samples that reflect an entire database. This research investigates whether the optimum allocation is suitable to extract representative samples depending on their variability within the groups in the input EEG data. It also assesses whether these samples are efficient for the multiclass least square support vector machine (MLS-SVM) to classify EEG signals. The performances of the MLS-SVM with four different output coding approaches: minimum output codes (MOC), error correcting output codes (ECOC), One vs One (1vs1) and One vs All (1vsA), are evaluated with a benchmark epileptic EEG database. To test the consistency, all experiments are repeated ten times with the same classifying parameters in each classification process. The results show very high classification performances for each class, and also confirm the consistency of the proposed method in each repeated experiment. In addition, the performances by the optimum allocation based MLS-SVM method are compared with the four existing reference methods using the same database. The outcomes of this research demonstrate that the optimum allocation is very effective and efficient for extracting the representative patterns from the multiclass EEG data, and the MLS-SVM is also very well fitted with the optimum allocation technique for the EEG classification.