EEG data classification with localised structural information

This paper develops a polygon feature selection method for the classification of temporal data from two or more sources on the basis of quantifying structural changes with time. The study focuses on the analysis of EEG data. The paper shows results on the feature classification using a modified fuzzy nearest neighbour method. The transformed inputs are ideally suited for the effective classification of EEG data. The results show that the developed polygon feature selection method can be used robustly in signal applications for source separation. Recognition rates vary for each EEG channel data between 90-99% correct recognition. It is expected that several applications including time-series analysis, signal processing and speech recognition will benefit from the findings in this paper