Abstract #5397

Empirical Model Decomposition Removes Non-stationary EEG Noise in Simultaneous fMRI-EEG Acquisition

Kevin Wen-Kai Tsai¹, Hsin-Ju Lee², Wen-Jui Kuo², Jo-Fu Lotus Lin³, and Fa-Hsuan Lin^3,4

¹Aim for the Top University Project Office, National Taiwan Normal University, Taipei, Taiwan, ²Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan, ³Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan, ⁴Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland

Non-stationary EEG noise from simultaneous fMRI-EEG acquisition could be conventionally removed by optimal basis selection and followed by a low-pass filtering. An empirical model decomposition (EMD) method was applied to partially remove non-stationary EEG noise from simultaneous fMRI-EEG acquisition. Our results suggested that EMD method could reveal similar auditory evoked potential with optimal basis selection and low-pass filtering without prior knowledge or cut-off frequency, thus preserving high frequency signal not empirically related to the non-stationary noise. This EMD method allows us to investigate human brain high frequency EEG oscillation in the simultaneous fMRI-EEG measurement.

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