Single Trial Evoked Potentials Estimation by Using Wavelet Enhanced Principal Component Analysis Method

Authors:
Ling Zou;Zhenghua Ma;Shuyue Chen;Suolan Liu;Renlai Zhou
Affiliations:
School of Information Science & Engineering, Jiangsu Polytechnic University, Changzhou, China 213164 and State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Bei ...;School of Information Science & Engineering, Jiangsu Polytechnic University, Changzhou, China 213164;School of Information Science & Engineering, Jiangsu Polytechnic University, Changzhou, China 213164;School of Information Science & Engineering, Jiangsu Polytechnic University, Changzhou, China 213164;School of Information Science & Engineering, Jiangsu Polytechnic University, Changzhou, China 213164 and State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Bei ...
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

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Abstract

In this paper we present a new wavelet denoising (WD) enhanced principal component analysis (PCA) method (wPCA) to reduce the number of trials required for the efficient extraction of brain event related potentials (ERPs). First, the ERPs are extracted with wavelet transform, giving us an enhanced version of the raw data. Next, the principal components (PCs) with most of the total variance are considered to be part of the ERP subspace. Lastly, the ERPs are reconstructed from the selected PCs. Simulation and experimental results show that the wPCA method provides better performance than either WD or PCA method.