Multiple ocular artifacts removal based on high-order statistical tensor algorithm

The aim of this paper is to propose a multiple ocular artifacts (OAs) removal method using the high order tensor algorithm. Four categories high order tensor methods are adopted to separate the real electroencephalogram (EEG) and ocular signals by underdetermined blind source separation (UBSS) model...

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Veröffentlicht in:2018 3rd International Conference on Intelligent Green Building and Smart Grid (IGBSG) S. 1 - 4
Hauptverfasser: Ge, Sunan, Yang, Yan, Ni, Wei, Zhang, Rui
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2018
Schlagworte:
Brain modeling
Correlation coefficient
EEG
Electroencephalography
Electrooculography
Mathematical model
ocular artifacts
Principal component analysis
Tensile stress
tensor
underdermined blind source separation
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Zusammenfassung:The aim of this paper is to propose a multiple ocular artifacts (OAs) removal method using the high order tensor algorithm. Four categories high order tensor methods are adopted to separate the real electroencephalogram (EEG) and ocular signals by underdetermined blind source separation (UBSS) model. The correlation coefficient and the non-negativity are adopted to choose the suitable UBSS algorithm. The ocular components are identified by the kurtosis value after separating between EEG and ocular signals. Then, the free-ocular sources components are reconstructed to EEG without OAs. The simulations show that the proposed method can effectively remove the OAs. At the same time, it also can retain the useful information after removing OAs.
DOI:10.1109/IGBSG.2018.8393530