Holo-Hilbert Spectral-based Removal of Gamma-Band Noise from Simultaneous EEG-fMRI Recordings

Simultaneous EEG-fMRI is a growing and promising field, as it has great potential to further our understanding of the spatiotemporal dynamics of brain function in health and disease. In particular, there is much interest in understanding the fMRI correlates of brain activity in the gamma band (30-100 Hz), as these frequencies are thought to be associated with cognitive processes involving perception, attention and memory, as well as with disorders such as schizophrenia and autism. However, progress in this area has been limited due to the MR-induced artifacts in EEG recordings, which seem to be more problematic for gamma frequencies. This paper presents a method of MR-induced noise removal from the gamma band of EEG that is based on Holo-Hilbert spectral analysis (HHSA), but with a new implementation strategy. HHSA uses a nested empirical mode decomposition (EMD) to identify amplitude and frequency modulations (AM and FM, respectively). Instead of including all FM components, our method examines only gamma-band FM and AM components, removes components with very low power based on the power-instantaneous frequency spectrum, and subsequently reconstructs the denoised gamma-band signal from the remaining components. Simulations demonstrate that our proposed method effectively reduces artifacts while preserving the original signal.