An Estimation Method for Multi‐channel EEG Data Based on Canonical Correlation Analysis
Yan Ling Zheng, Xiaojiao Wan, Chaodong Ling · Chinese Journal of Electronics · 2015
Electroencephalogram (EEG) signal is oftencontaminated by electronic noise as well as movementartifacts. This paper presented an algorithm basedon Canonical correlation analysis (CCA) to estimate multichannelEEG data. Different from previous studies, inwhich CCA was mainly used to detect the invariant featuresspecific to each brain state, in this paper, the canonicalvariates computed by CCA were used to reconstructthe multi-channel EEG data. Firstly, two data sets, EEGsignals and the reference signals based on prior knowledgewere constructed. Next, canonical variates were computedby projecting the two data sets onto basis vectors. Finally,a least squares solution was used to estimate the multichannelEEG data. The experiment results suggested thatthe algorithm is capable of reconstructing the actual specificcomponents with high quality. We also hint futurepossible application of the algorithm in the estimation offunctional connectivity patterns at the end of the paper.