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Apply a zero-phase low-pass, high-pass, band-pass, or band-stop FIR or IIR filter.

Source: R/filters.R

Apply a low-pass, high-pass, band-pass, or band-stop filter to every segment of an eeg_lst. These filters are adapted from the FIR and IIR filters in MNE package (v 0.0.17.1) of python. For background information about the FIR vs IIR filters, see here.

eeg_filt_low_pass(.data, ..., .freq = NULL, .config = list(), na.rm = FALSE)

eeg_filt_high_pass(.data, ..., .freq = NULL, .config = list(), na.rm = FALSE)

eeg_filt_band_pass(.data, ..., .freq = NULL, .config = list(), na.rm = FALSE)

eeg_filt_band_stop(.data, ..., .freq = NULL, .config = list(), na.rm = FALSE)

Arguments

.data

A channel or an eeg_lst.

...

Channels to apply the filters to. All the channels by default.

.freq

A single cut frequency for eeg_filt_low_pass and eeg_filt_high_pass, two edges for eeg_filt_band_pass and eeg_filt_band_stop.

.config

Other parameters passed in a list to configure the filters. See details for options.

na.rm

=TRUE will set to NA the entire segment that contains an NA, otherwise the filter will stop with an error.

Value

A channel or an eeg_lst.

Details

  • eeg_filt_low_pass() Low-pass or high-cut filter.

  • eeg_filt_high_pass() High-pass or low-cut filter.

  • eeg_filt_band_pass() Band-pass filter.

  • eeg_filt_band_stop() Band-stop filter.

After setting a filter method. The filters can be configured passing a list to the argument .config.

Using method ="fir", we define a zero-phase filter of the FIR (finite impulse response) class (MNE v 0.0.17.1 default); these are the options:

  • l_trans_bandwidth = "auto" by default. This is min(max(l_freq * 0.25, 2), l_freq), where l_freq is the freq of the high pass filter, or freq[1] of a band pass/stop filter.

  • h_trans_bandwidth = "auto" by default. This is min(max(0.25 * h_freq, 2.), sampling_rate / 2. - h_freq) where h_freq is the freq of the low pass filter, or freq[2] of a band pass/stop filter.

Using method = "iir", we define a zero-phase (two-pass forward and reverse) non-causal IIR filter. Filter type is Butterworth by default, and either order (default) or maximum loss and attenuation (gpass and gstop) should be specified:

  • type = "butter" for Butterworth by default, other options are "cheby1", or "cheby2" for Chebyshev type I or type II, or "ellip" for Elliptic.

  • order = 6 by default for low and high pass, and 4 by default for band pass and stop filters (this follows the defaults of Fieldtrip matlab package). Notice that the effective order after forward-backward pass is multiplied by two.

  • gpass : The maximum loss in the passband (dB).

  • gstop : The minimum attenuation in the stopband (dB).

See also

Other preprocessing functions: eeg_baseline(), eeg_downsample(), eeg_ica_keep(), eeg_ica(), eeg_rereference(), eeg_segment()

Examples

library(dplyr)
library(ggplot2)
data("data_faces_ERPs")
data_ERPs_filtered <- data_faces_ERPs %>%
  eeg_filt_low_pass(.freq = 1)
#> Setting up low-pass filter at 1 Hz
#> Width of the transition band at the high cut-off frequency is 2 Hz
# Compare the ERPs
data_faces_ERPs %>%
  eeg_select(O1, O2, P7, P8) %>%
  plot() +
  facet_wrap(~.key)


data_ERPs_filtered %>%
  eeg_select(O1, O2, P7, P8) %>%
  plot() +
  facet_wrap(~.key)