Semi-Supervised Clustering Approach for P300 based BCI Speller Systems

The paper presents a k-means based semi-supervised clustering approach for recognizing and classifying P300 signals for BCI Speller System. P300 signals are proved to be the most suitable Event Related Potential (ERP) signal, used to develop the BCI systems. Due to non-stationary nature of ERP signals, the wavelet transform is the best analysis tool for extracting informative features from P300 signals. The focus of the research is on semisupervised clustering as supervised clustering approach need large amount of labeled data for training, which is a tedious task. Hence works for small-labeled datasets to train classifiers. On the other hand, unsupervised clustering works when no prior information is available i.e. totally unlabeled data. Thus leads to low level of performance. The in-between solution is to use semi-supervised clustering, which uses a few labeled with large unlabeled data causes less trouble and time. The authors have selected and defined adhoc features and assumed the Clusters for small datasets. This motivates us to propose a novel approach that discovers the features embedded in P300 (EEG) signals, using an k-means based semisupervised cluster classification using ensemble SVM.