A Background EEG Removal Method Combining PCA with Multivariate Empirical Mode Decomposition for Event-Related Potential Measurements

作者：Kawaguchi Hirokazu^*; Kume Takahiro; Kobayashi Tetsuo

来源：IEEJ Transactions on Electrical and Electronic Engineering, 2013, 8(S1): S53-S60.

DOI：10.1002/tee.21918

• event-related potential
• principal component analysis
• multivariate empirical mode decomposition
• EEG

The event-related potential (ERP) is a neural response to an internal or external event, and can be obtained by averaging time-locked scalp potentials. The ERP measured in a single trial often has a low signal-to-noise ratio (SNR) because of the relatively large background due to the rhythmic electroencephalogram (EEG) noise. This paper proposes a novel method to enhance ERPs by combining principal component analysis (PCA) with multivariate empirical mode decomposition (M-EMD). EMD is a data-driven time-frequency analysis of nonlinear and nonstationary signals, and M-EMD is its multivariate extension. In the proposed method, PCA reduces the data dimensions, while M-EMD removes the relatively large background EEGs. The performance of the method is evaluated with simulated and measured P300 ERP components obtained from a visual oddball experiment. The results demonstrate that the proposed method can substantially reduce the background EEGs and improve the SNR of P300s.

• 出版日期2013