EFFICIENT CLASSIFICATION USING MULTIPLE MENTAL THOUGHTS
Abstract
Researches in personal identification show that classification using multiple mental thoughts increases complexity and system’s processing time. In this paper, an efficient classification algorithm is proposed to classify an individual using multiple mental thoughts. Features from Electroencephalography (EEG), used as biometric, are extracted using sixth order Autoregressive (AR) model, and Linear Discriminant Analysis (LDA) based classification is performed based on best mental thought combinations. Matlab® simulation results indicate that the proposed algorithm reduces the complexity as well as the processing time that confirms the use of EEG as a biometric for personal identification.References
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