A robust algorithm for removing artifacts in EEG recorded during FMRI/EEG study
The main purpose of this study was to propose a robust algorithm for removing artifacts from the electroencephalographic (EEG) data collected during magnetic resonance imaging (MRI). The core idea of the proposed method was to remove the main gradient artifacts by the maximum cross-correlation method and to remove the residual artifacts by the rolling-ball algorithm and lowpass filtering. The results showed that the proposed algorithm had a better performance and was robust in the sense that its performance was maintained when the sampling rate of EEG data was decreased from 10KHz to 200Hz.
magnetic resonance imaging gradient artifact cross-correlation rolling-ball algorithm electroencephalography