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Title
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Efficient Classification using Multiple Mental Thoughts
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Sub-Title |
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Subject |
"Autoregressive (AR), Mental Thoughts (MT), Electroencephalography (EEG), Linear Discriminant Analysis (LDA)"
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Sub-Subject |
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Author |
A. Zafar, M. I. Ishtiaq and Aamir Hanif,
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Publish Year |
2013 |
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Supervisor |
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Diss#. |
- |
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Chapters |
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Pages |
45-51 |
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Text Language |
English |
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Accession |
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Library Section |
Research Article |
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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.
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