Reducing power line noise in EEG and MEG data via spectrum interpolation.


Center of Functionally Integrative Neuroscience, Aarhus University, 8000, Aarhus, Denmark; Department of Psychology, University of Konstanz, 78457, Konstanz, Germany; Department of Psychology, Faculty of Social Sciences, University of Oslo, 0373, Oslo, Norway. Electronic address: [Email]


Electroencephalographic (EEG) and magnetoencephalographic (MEG) signals can often be exposed to strong power line interference at 50 or 60 Hz. A widely used method to remove line noise is the notch filter, but it comes with the risk of potentially severe signal distortions. Among other approaches, the Discrete Fourier Transform (DFT) filter and CleanLine have been developed as alternatives, but they may fail to remove power line noise of highly fluctuating amplitude. Here we introduce spectrum interpolation as a new method to remove line noise in the EEG and MEG signal. This approach had been developed for electromyographic (EMG) signals, and combines the advantages of a notch filter, while synthetic test signals indicate that it introduces less distortion in the time domain. The effectiveness of this method is compared to CleanLine, the notch (Butterworth) and DFT filter. In order to quantify the performance of these three methods, we used synthetic test signals and simulated power line noise with fluctuating amplitude and abrupt on- and offsets that were added to an MEG dataset free of line noise. In addition, all methods were applied to EEG data with massive power line noise due to acquisition in unshielded settings. We show that spectrum interpolation outperforms the DFT filter and CleanLine, when power line noise is nonstationary. At the same time, spectrum interpolation performs equally well as the notch filter in removing line noise artifacts, but shows less distortions in the time domain in many common situations.


Artifact removal,Gibbs effect,Notch filter,Power line noise,Ringing,Spectrum interpolation,