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- W2130542275 abstract "The problem of identifying a person using biometric data may be of interest. In this paper,EEG signals are used to identify a person as different persons have different EEG patterns. EEG signals can be measured from different locations. Too many signals can degrade the recognition speed and accuracy. A practical technique combining independent component analysis (ICA) for signal cleaning and a supervised neural network for classifying signals is proposed. From 16 EEG different signal locations, three truly relevant locations FP1, P3, and C4 were selected. This selection is based on signals obtained from the subjects at the Comprehensive Epilepsy Program of Chulalongkorn University Hospital, Bangkok, Thailand." @default.
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- W2130542275 date "2009-01-01" @default.
- W2130542275 modified "2023-09-23" @default.
- W2130542275 title "Selecting Relevant EEG Signal Locations for Personal Identification Problem Using ICA and Neural Network" @default.
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- W2130542275 doi "https://doi.org/10.1109/icis.2009.156" @default.
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