NDLI: Design of a radial basis function neural network for attention tasks event related potentials extraction

Content Provider	IEEE Xplore Digital Library
Author	Liu Mingyu Wang Jue Yan Nan
Copyright Year	2005
Description	Author affiliation: Key Lab. of Biomed. Eng., Xi'an Jiaotong Univ., China (Liu Mingyu; Wang Jue; Yan Nan)
Abstract	Electroencephalogram (EEG) based biofeedback is widely employed to treat certain kinds of diseases especially Attention Deficit Hyperactivity Disorder (ADD/ADHD). Thus to design a system capable of learning a particular mapping between EEG features and different attention-level mental tasks is of great significance. Event Related Potentials (ERP) is such a powerful feature which is traditionally extracted by averaging. The paper proposed a new ERP extraction algorithm using radial basis function (RBF) neural network. It discussed the configuration, learning and running of the designed network. In order to reduce computational complexity and the influence of noise in estimating ERP, the partial least square regression was introduced to train the RBF network. Series experiments showed that the method is effective and is suitable for single-trail ERP estimation.
Sponsorship	Huazhong Univ. of Sci. and Technol. (China) Arizona State Univ. (USA) Tsinghua Univ. (China)
Starting Page	100
Ending Page	103
File Size	543077
Page Count	4
File Format	PDF
ISBN	0780389026
DOI	10.1109/ICNIC.2005.1499852
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Humans Radial basis function networks Signal processing Cathode ray tubes Displays Brain modeling Enterprise resource planning Electroencephalography Least squares methods
Content Type	Text
Resource Type	Article

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