NDLI: ECoG-based brain-computer interface using relative wavelet energy and probabilistic neural network

Content Provider	IEEE Xplore Digital Library
Author	Hai-bin Zhao Chun-yang Yu Chong Liu Hong Wang
Copyright Year	2010
Description	Author affiliation: School of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning Province, China (Hai-bin Zhao; Chun-yang Yu; Chong Liu; Hong Wang)
Abstract	In this study, a brain-computer interface (BCI) using electrocorticograms (ECoG) is proposed. Feature extraction is an important task that significantly affects the classification results. First, the discrete wavelet transform was applied to ECoG signals from one subject performing imagined movements of either the left small-finger or the tongue. After preprocessing, relative wavelet energy of selected 8 channels were extracted and built 40 dimension feature vector. Then the dimension of feature vector was reduced using principal component analysis (PCA). Finally, probabilistic neural network (PNN) was used to classify. The average classification accuracy rate reached a maximum of 91.8% when spread of radial basis functions was 0.11. The offline analysis results showed that ECoG signals could be used in BCI design, and gave new ideas and methods for feature extraction and classification of imaginary movements in ECoG-based BCI research.
Starting Page	873
Ending Page	877
File Size	233686
Page Count	5
File Format	PDF
ISBN	9781424464951
e-ISBN	9781424464982
DOI	10.1109/BMEI.2010.5639897
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Accuracy probabilistic neural network feature extraction Support vector machine classification brain-computer interface Feature extraction Probabilistic logic relative wavelet energy Brain computer interfaces Principal component analysis
Content Type	Text
Resource Type	Article

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