Robust Electroencephalogram Phase Estimation with Applications in Brain-computer Interface Systems
Résumé
In this study, a robust method is developed for frequency-specific electroencephalogram (EEG) phase extraction using the analytic representation of the EEG. Using recent theoretical findings in this area, it is shown that some of the phase variations— previously associated to the brain response— are systematic side-effects of the methods used for EEG phase calculation, especially during low analytical amplitude segments of the EEG. With this insight, the proposed method generates randomized ensembles of the EEG phase using minor perturbations in pole-zero loci of narrow-band zero-phase IIR filters, followed by phase estimation using the signal's analytical form and ensemble averaging to obtain a robust EEG phase. This Monte Carlo method is shown to be very robust to noise and minor changes of the filter parameters and reduces the effect of fake EEG phase jumps (without a cerebral source). As proof of concept, the proposed method is used for extracting EEG phase features in brain computer interface (BCI) classification. The results show significant improvement in classification rates using rather simple phase-related features over a standard BCI dataset. The proposed method for EEG phase calculation is very generic and may be applied for all EEG phase-related studies.
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