NDLI: An Estimation Method for Multi-channel EEG Data Based on Canonical Correlation Analysis

Content Provider	IET Digital Library
Author	Yan, Zheng Wan, Xiaojiao Ling, Chaodong
Abstract	Electroencephalogram (EEG) signal is often contaminated by electronic noise as well as movement artifacts. This paper presented an algorithm based on Canonical correlation analysis (CCA) to estimate multichannel EEG data. Different from previous studies, in which CCA was mainly used to detect the invariant features specific to each brain state, in this paper, the canonical variates computed by CCA were used to reconstruct the multi-channel EEG data. Firstly, two data sets, EEG signals and the reference signals based on prior knowledge were constructed. Next, canonical variates were computed by projecting the two data sets onto basis vectors. Finally, a least squares solution was used to estimate the multichannel EEG data. The experiment results suggested that the algorithm is capable of reconstructing the actual specific components with high quality. We also hint future possible application of the algorithm in the estimation of functional connectivity patterns at the end of the paper.
Starting Page	569
Ending Page	572
Page Count	4
ISSN	10224653
Volume Number	24
e-ISSN	20755597
Issue Number	Issue 3, Jul (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/24/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2015.07.022
Journal	Chinese Journal of Electronics
Publisher Date	2015-07-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Basis Vector Bioelectric Signal Biology And Medical Computing Brain State Canonical Correlation Analysis Canonical Variates CCA Correlation Method Data Sets Digital Signal Processing Electrodiagnostics And Other Electrical Measurement Technique Electroencephalogram Signal Electroencephalography Electronic Noise Estimation Method Estimation Theory Functional Connectivity Pattern Interpolation And Function Approximation Invariant Feature Least Squares Approximation Least Squares Solution Linear Algebra Medical Signal Processing Movement Artifacts Multichannel EEG Data Numerical Analysis Numerical Approximation And Analysis Probability Theory Reference Signal Signal Processing And Detection Statistics Stochastic Linearised SCUC Vector
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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