A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings (2023, unknown): “However, due to the nature of the speech-induced vibration artifact, which overlaps in frequency with high-gamma activity, ICA may fail to decompose artifactual from neural sources 22 (see also Results Section).”
Resting-state alpha and mu rhythms change shape across development but lack diagnostic sensitivity for ADHD and autism (2025, unknown): “Following the procedure of Nikulin and colleagues (Nikulin, Nolte, and Curio, 2011), X N was calculated by bandpass filtering ( α − 4, α + 4) and then bandstop filtering ( α − 3, α + 3) .”
Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations (2021, methodology): “Thus we suggest that existing methods could be enhanced by more elaborate data cleaning, such as spatio-spectral decomposition (SSD) (Nikulin et al., 2011), independent component analysis (ICA), or inverse modeling.”