A study on classification of EEG Data using the Filters

Abstract

In the field of data mining, classification of data is being a difficult task for further analysis. Classifying the EEG data would require more efficient algorithms. In this paper the classification filters such as Fast Hartley Transform (FHT) and Chebyshev filters are used to classify the EEG data signals. In a bulk data set of EEG signals, the signals are classified into many channels. Though various filters are available for classification, FHT with Chebyshev and FT tree only are taken to know the efficiency in classifying the EEG data signals. When these filters are applied to the data instances the percentage of correctly classified instances is high. Based on the experimental result it is suggested that these filters could be used for the enhancement of classification of EEG data.