Title :
EEG Recognition Based on Multiple Types of Information by Using Wavelet Packet Transform and Neural Networks
Author :
Wang, Baojun ; Jun, Liu ; Bai, Jing ; Le Peng ; Li, Yan ; Li, Guang
Author_Institution :
Dept. of Biomed. Eng., Zhejiang Univ., Hangzhou
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
Keywords :
Gaussian processes; backpropagation; bioelectric potentials; electroencephalography; feature extraction; handicapped aids; medical signal processing; perceptrons; signal classification; support vector machines; time-frequency analysis; wavelet transforms; EEG recognition; Gaussian kernel; SVM; backpropagation; beta-band; binary classification; brain computer interface; feature extraction; neural networks; perceptron; slow cortical potentials; support vector machines; time-frequency domain; wavelet packet transform; Biological neural networks; Brain computer interfaces; Electroencephalography; Feature extraction; Neural networks; Support vector machine classification; Support vector machines; Testing; Wavelet packets; Wavelet transforms;
Conference_Titel :
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location :
Shanghai
Print_ISBN :
0-7803-8741-4
DOI :
10.1109/IEMBS.2005.1615697