MEG (Magnetoencephalography) Program

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Digital filtering


Data filtering is a conceptually simple, though powerful technique to extract signals within a predefined frequency band of interest. This off-line data pre-processing step is the realm of digital filtering: an important and sophisticated subfield of electrical engineering (Hamming, 1983). Applying a filter to the data presupposes that the information carried by signals will be mostly preserved, to the benefit of attenuating other frequency components of supposedly, no interest.


Not every digital filter is suitable to the analysis of MEG/EEG traces. Indeed, the performances of filters are defined from basic characteristics such as the attenuation outside the bandpass of the frequency response, stability, computational efficiency and most importantly, the introduction of phase delays. This latter is a systematic by-product of filtering and some filters may be particularly inappropriate in that respect: infinite impulse response (IIR) digital filters are usually more computationally efficient than finite impulse response (FIR) alternatives, but with the inconvenient of introducing non-linear frequency-dependent phase delays; hence some non-equal delays in the temporal domain at all frequencies, which is unacceptable for MEG/EEG signal analysis where timing and phase measurements are crucial. FIR filters delay signals in the time domain equally at all frequencies, which can be conveniently compensated for by applying the filter twice: once forward and once backward on the MEG/EEG time series (Oppenheim, Schafer, & Buck, 1999).

Note however some possible edge effects of the FIR filter at the beginning and end of the time series, and the necessity of a large number of time samples when applying filters with low high-pass cutoff frequencies (as the length of the filter’s FIR increases). Hence it is generally advisable to apply digital high-pass filters on longer episodes of data, such as on the original ‘raw’ recordings, before these latter are chopped into epochs of shorter durations about each trial for further analysis.
 

Bringing more details to the discussion would reach outside the scope of these pages. The investigator should nevertheless be well aware of the potential pitfalls of analysis techniques in general, and of digital filters in particular. Although commercial software tools are well equipped with adequate filter functions, in-house or academic software solutions should be first evaluated with great caution.

Digital band-pass filtering applied to spontaneous MEG data during an interictal epileptic spike event (total epoch of 700ms duration, sampled at 1KHz). The time series of 306 MEG sensors are displayed using a butterfly plot, whereby all waveforms are overlaid within the same axes. The top row displays the original data with digital filters applied during acquisition between 1.5 and 330Hz. The bottom row is a pre-processed version of the same data, band-passed filtered between 2 and 30Hz. Note how this version of the data better reveals the epileptic event occurring about time t=0ms. The corresponding sensor topographies of MEG measures are displayed to the right. The gray scale display represents the intensity of the magnetic field captured at each sensor location and interpolated over a flattened version of the MEG array (nose pointing upwards). Note also how digital band-pass filtering strongly alters the surface topography of the data, by revealing a simpler dipolar pattern over the left temporo-occipital areas of the array.
 


Copyright 2010 Sylvain Baillet, PhD

 

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