Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system

By Jasmin Kevric, Abdulhamit Subasi. Biomedical Signal Processing and Control, 2017

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EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019, methodology): “Kevric & Subasi, 2017 [11] Empirical mode decomposition, DWT, WPD 4 Kaiser criterion k-NN 92.”
Removal of Artifacts from EEG Signals: A Review (2019, methodology): “The nonlinear filtering techniques Multiscale Principal Component Analysis (MSPCA) will preserve substantial amount of changes in the recordings and provide enhanced artifact re oval erfor ance than sole algorith [108].”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, methodology): “In Kevric and Subasi (2017), three distinct decomposition techniques, namely, WPD, EMD, and DWT, have been investigated to gain optimum accuracy.”
Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition (2020, background): “…proposed a probabilistic CSP as a generic EEG spatio-temporal modeling framework successfully resolving the overﬁtting issue of CSP. Filter bank CSP (FBCSP) combining multiple frequency bandpass ﬁlters and CSP has been demonstrated competitive performance in several EEG decoding competitions [26].”