Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram

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How researchers see this paper

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EEG artifact removal—state-of-the-art and guidelines (2015, methodology): “On the basis of experimentation, we believe SOBI, extended InfoMax and AMICA are as good as CCA at removing myogenic activity from the EEG.”
Artifact Removal in Physiological Signals—Practices and Possibilities (2012, background): “CCA solves the BSS problem by forcing the sources to be maximally autocorrelated and mutually uncorrelated [91].”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, methodology): “…as well as more modern attempts, for instance, the wavelet transform (WT) method (Punsawad and Wongsawat, 2017), empirical mode decomposition (EMD) (Zhang et al., 2008), Canonical Correlation Analysis (CCA) (de Clercq et al., 2006), and non-linear mode decomposition (NMD) (Iatsenko et al., 2015).”
Removal of Artifacts from EEG Signals: A Review (2019, methodology): “The CCA has been applied to remove muscle artifacts from EEG signals has been ﬁrst discussed in [64].”
Multimodal assessment of acute stress dynamics using an aversive video paradigm (AVP) (2024, unknown): “We also used the automatic artifact removal (AAR) plugin of EEGLAB to remove eye and muscle activity (De Clercq et al., 2006; G ´ omez-Herrero et al., 2006).”