Introduction

Downloading a bdf

Two example Biosemi bdf files can be downloaded from the producer’s website. To download and unzip example bdf files use:

url_bdf_zip <- "https://www.biosemi.com/download/BDFtestfiles.zip"
temp <- tempfile()
download.file(url_bdf_zip, temp)
unzip(temp)
unlink(temp)

Loading a bdf file

library(eegUtils)
library(dplyr)

bdf_filename <- "Newtest17-256.bdf"
bdf_data <- import_raw(bdf_filename)

The file is now loaded. You can see its structure

str(bdf_data)

## List of 7
##  $ signals   :Classes 'tbl_df', 'tbl' and 'data.frame':  15360 obs. of  16 variables:
##   ..$ A1 : num [1:15360] -527 -522 -526 -529 -532 ...
##   ..$ A2 : num [1:15360] -595 -593 -589 -585 -582 ...
##   ..$ A3 : num [1:15360] -124 -122 -119 -115 -111 ...
##   ..$ A4 : num [1:15360] -774 -772 -768 -764 -760 ...
##   ..$ A5 : num [1:15360] -467 -465 -461 -457 -453 ...
##   ..$ A6 : num [1:15360] -646 -644 -640 -636 -632 ...
##   ..$ A7 : num [1:15360] -611 -609 -605 -600 -597 ...
##   ..$ A8 : num [1:15360] 49.6 52 54.4 59.6 63.3 ...
##   ..$ A9 : num [1:15360] -104.6 -102.6 -99.4 -94.6 -91 ...
##   ..$ A10: num [1:15360] -353 -350 -346 -342 -338 ...
##   ..$ A11: num [1:15360] -352 -349 -346 -342 -338 ...
##   ..$ A12: num [1:15360] -133 -132 -129 -123 -120 ...
##   ..$ A13: num [1:15360] -171 -169 -166 -162 -158 ...
##   ..$ A14: num [1:15360] -654 -652 -647 -644 -641 ...
##   ..$ A15: num [1:15360] -574 -572 -569 -564 -561 ...
##   ..$ A16: num [1:15360] -224 -221 -218 -213 -209 ...
##  $ srate     : num 256
##  $ events    :Classes 'tbl_df', 'tbl' and 'data.frame':  40 obs. of  3 variables:
##   ..$ event_onset: num [1:40] 415 823 1197 1590 2012 ...
##   ..$ event_time : num [1:40] 1.62 3.21 4.67 6.21 7.86 ...
##   ..$ event_type : num [1:40] 255 255 255 255 255 255 255 255 255 255 ...
##  $ chan_info : NULL
##  $ timings   :Classes 'tbl_df', 'tbl' and 'data.frame':  15360 obs. of  2 variables:
##   ..$ sample: int [1:15360] 1 2 3 4 5 6 7 8 9 10 ...
##   ..$ time  : num [1:15360] 0 0.00391 0.00781 0.01172 0.01562 ...
##  $ continuous: logi TRUE
##  $ reference : NULL
##  - attr(*, "class")= chr "eeg_data"

Pre-processing

eegUtils allows loading the bdf files, selecting channels

my_data <- bdf_data %>%
  select_elecs(c("A1", "A2"), keep = FALSE) %>% # remove channels
  reref_eeg(ref_chans = "average") %>% # re-reference
  iir_filt(low_freq = 0.1, high_freq = 40) %>% # band-pass filter
  epoch_data(255, c(-.2,.2)) # epoch: choose a trigger and time limits

## Band-pass IIR filter from 0.1-40 Hz

Figures

Create a butterfly plot showing data from all the electrodes

plot_butterfly(my_data, baseline = c(-.2, 0), time_lim = c(-.1, .2), legend = FALSE)

Or the timecourse of a single electrode

plot_timecourse(my_data, electrode = "A3", baseline = c(-.2,0), time_lim = c(-.1, .2))

How about an EEGLAB style ERP image?

erp_image(my_data, electrode = "A9")