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Classification of EEG-P300 signals using Fisher’s linear discriminant analysis

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Abstract

In this paper, a classifier using Fisher’s Linear Discriminant Analysis is used to investigate the performance of three different extraction methods for brain signal based electroencephalogram (EEG)-P300. EEG-P300 recordings provide an important means of brain-computer communication, but their classification accuracy and transfer rate are limited by unexpected signal variations due to artifacts and noises. A comparison of extraction methods (i.e., AAR, JADE, and SOBI) entailing time-series EEG signals is presented. Finally, the promising results reported here reflect the considerable potential of EEG for the continuous classification of mental states.

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Instrumentation Control and Automation (ICA), 2013 3rd International Conference on

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