Artifact Removal in Physiological Signals—Practices and Possibilities

By Kevin T. Sweeney, Tomás Ward, Seán F. McLoone. IEEE Transactions on Information Technology in Biomedicine, 2012

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Removal of Artifacts from EEG Signals: A Review (2019, methodology): “…domains. decomposing the EEG signal into other domains Alternatively, these techniques range from Regression [12], Blind Source Separation (BSS) [13–15 Empirical-mode Decomposition (EMD), Wavelet Transform Alternatively, these techniques range from Regression [12], Blind Source Separation (BSS)…”
EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, background): “rithms for signal enhancement, blind source separation (BSS) estimates original sources and parameters of a mixing system and removes the artefact signals, such as eye blinks and movement, present in the sources [42].”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, methodology): “However, these are only eﬀective when the frequency bands of the signal do not overlap (Sweeney et al., 2012).”
EEG artifact removal—state-of-the-art and guidelines (2015, methodology): “Bayes filtering is a probabilistic system estimation method starting from noisy observations, based on assuming that the system is Markov [19].”