EEG recognition based on multiple types of information by using wavelet packet transform and neural networks

Baojun Wang, J. Bai, L. Peng, G. Li

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this work, we proposed a method for a binary classification in an EEG-based brain computer interface (BCI) with wavelet packet transform and neural networks. For feature extraction, we introduced a new method which combined the slow cortical potentials (SCPs) and the specific energy from the time-frequency domain in beta-band via the wavelet packet transform. A 3-layer perceptron established by back-propagation and the support vector machines (SVMs) were utilized for classification, respectively. We compared the performance in terms of changing the architecture of the net. The accuracy of BP network was found to be best with 4-5-1 architecture reaching an accuracy of 91.47% on test set. Meanwhile, a SVM with Gaussian kernel revealed an accuracy of 91.13% on the test set, showing that multiple types of information have great advantage over other features.
Original languageEnglish
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
PublisherIEEE Engineering in Medicine and Biology Society
Pages5377-5380
Number of pages4
ISBN (Print)9780780387409
Publication statusPublished - 1 Jan 2006

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