Key result
Incremental Dynamic Mode Decomposition algorithms outperformed online DMD algorithms in predicting future EEG signals for error-related potentials, particularly when the data matrix was ill-conditioned.
Why the study?
For systems where underlying low-dimensional dynamics are time-varying, standard dynamic mode decomposition techniques providing time-invariant approximations may not be appropriate.
The study presents novel incremental Dynamic Mode Decomposition algorithms that effectively model time-varying systems, including EEG data.
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Incremental DMD aids EEG modeling; leaves open validation for cardiovascular signal applications.
Alfatlawi et al. (2020) studied Error-Related Potentials in EEG (n=6). Incremental Dynamic Mode Decomposition (DMD) algorithms vs. Online DMD algorithms was evaluated on Normalized Root Mean Square (RMS) prediction error. Incremental Dynamic Mode Decomposition algorithms outperformed online DMD algorithms in predicting future EEG signals for error-related potentials, particularly when the data matrix was ill-conditioned.
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