Digital filter design for electrophysiological data--a practical approach

Background: Filtering is a ubiquitous step in the preprocessing of electroencephalographic (EEG) and magnetoencephalographic (MEG) data. Besides the intended effect of the attenuation of signal components considered as noise, filtering can also result in various unintended adverse filter effects (distortions such as smoothing) and filter artifacts.

Method: We give some practical guidelines for the evaluation of filter responses (impulse and frequency response) and the selection of filter types (high-pass/low-pass/band-pass/band-stop; finite/infinite impulse response, FIR/IIR) and filter parameters (cutoff frequencies, filter order and roll-off, ripple, delay and causality) to optimize signal-to-noise ratio and avoid or reduce signal distortions for selected electrophysiological applications.

Results: Various filter implementations in common electrophysiology software packages are introduced and discussed. Resulting filter responses are compared and evaluated.

Conclusion: We present strategies for recognizing common adverse filter effects and filter artifacts and demonstrate them in practical examples. Best practices and recommendations for the selection and reporting of filter parameters, limitations, and alternatives to filtering are discussed.