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.
Highlights
Baby DeepaClassifying the EEG data would require more efficient algorithms
The EEG data set obtained from BCI is used for classification
The features extracted from EEG are not relevant and do not describe well the neurophysiologic signals employed, the classification algorithm which will use such features will have trouble in identifying the class of these features i.e., mental state of the user
Summary
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
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