,
Manipulate the signal table and the segments table of an eeg_lst with dplyr-like functions.
eeg_mutate(.data, ...)
eeg_transmute(.data, ...)
eeg_filter(.data, ..., .preserve = FALSE)
eeg_summarize(.data, ..., .groups = "keep")
eeg_summarise(.data, ..., .groups = "keep")
eeg_group_by(.data, ..., .add = FALSE, .drop = FALSE)
eeg_ungroup(.data, ...)
eeg_select(.data, ...)
eeg_rename(.data, ...)
eeg_rename_with(.data, .fn, .cols = where(is_channel_dbl), ...)
eeg_groups(x)
# S3 method for eeg_lst
eeg_groups(x)
eeg_group_vars(x)
eeg_anti_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
eeg_left_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
eeg_semi_join(x, y, by = NULL, suffix = c(".x", ".y"), ...)
eeg_pull(.data, var = -1, name = NULL, ...)
across(.cols = everything(), .fns = NULL, ..., .names = NULL)
across_ch(.fns = NULL, ..., .names = NULL)
c_across_ch()
c_across(.cols = everything())An eeg_lst.
Name-value pairs of expressions; see dplyr for more help.
Not in use, for compatibility reasons.
Only .groups = "keep" is available. Same grouping structure as .data.
When FALSE, the default, group_by() will override existing groups. To add to the existing groups, use .add = TRUE.
Only .drop = FALSE is available, empty groups are never dropped.
Function to transform the names with.
Columns to rename. Defaults to all columns. tidyselect compatible.
An eeg_lst.
A data frame, tibble, or data.table.
A character vector of variables to join by.
If NULL, the default, *_join() will perform a natural join, using all
variables in common across x and y. A message lists the variables so that you
can check they're correct; suppress the message by supplying by explicitly.
To join by different variables on x and y, use a named vector.
For example, by = c("a" = "b") will match x$a to y$b.
To join by multiple variables, use a vector with length > 1.
For example, by = c("a", "b") will match x$a to y$a and x$b to
y$b. Use a named vector to match different variables in x and y.
For example, by = c("a" = "b", "c" = "d") will match x$a to y$b and
x$c to y$d.
To perform a cross-join, generating all combinations of x and y,
use by = character().
If x and y are not from the same data source,
and copy is TRUE, then y will be copied into the
same src as x. This allows you to join tables across srcs, but
it is a potentially expensive operation so you must opt into it.
If there are non-joined duplicate variables in x and
y, these suffixes will be added to the output to disambiguate them.
Should be a character vector of length 2.
A variable specified as:
a literal variable name
a positive integer, giving the position counting from the left
a negative integer, giving the position counting from the right.
The default returns the last column (on the assumption that's the column you've created most recently).
This argument is taken by expression and supports quasiquotation (you can unquote column names and column locations).
An optional parameter that specifies the column to be used
as names for a named vector. Specified in a similar manner as var.
Functions to pass. Can pass a list of functions.
A glue specification that helps with renaming output columns.
{.col} stands for the selected column, and {.fn} stands for the name of the function being applied.
The default (NULL) is equivalent to "{.col}" for a single function case and "{.col}_{.fn}"
when a list is used for .fns.
An eeg_lst object.
Wrappers for dplyr's commands that act
eeg_lst objects. Functions that drop or rename columns won't remove columns starting with a dot. These functions are powered by data.table through tidytable.
The following wrappers act in a special way for eeg_lst objects:
eeg_*_join(): joins an external table to the segments table of the eeg_lst.
eeg_mutate() and eeg_transmute(): mutates the signal_tbl table when is applied to a channel (or a column of the signal table), and mutates the segments table otherwise. It can also mutates by reference.
eeg_summarize() summarizes the channel of the signal_tbl table.
eeg_pull() only pulls columns of the signal table
In addition, across(), and c_across() work as well. Notice that there are convienent wrappers across_ch() and c_across_ch() where the argument .cols is always set to where(is_channel_dbl).
These functions emulate dplyr functionality but they are actually powered by the wrapper of data.table, tidytable and some times they might be behave a bit differently than the dplyr counterpart.
The default values of the arguments might be different, and some arguments might not exist for the eeguana dplyr-like functions.
Grouped mutations behave slightly different than ungrouped ones: Channel properties are removed if the data is ungrouped and one does eeg_mutate(data, channel = 0), but not if the data is grouped.
eeguana's eeg_mutate doesn't allow to refer back to a recently created channel: data_eeg %>% eeg_mutate(X = F1 *2, Y = X) is not valid. One needs to do data_eeg %>% eeg_mutate(X = F1) %>% eeg_mutate(Y = X).
eeguana's eeg_mutate doesn't use the most updated value of a column from the same call. If X is a channel, then data_eeg %>% eeg_mutate(X = X *2, Y = X+1) will add 1 to the original value of X, and not to the latest one.
eeg_filter behaves similarly to dplyr's filter. If you want to filter the signal using IIR or FIR filters use eeg_filt* functions.
These functions not only edit the eeg_lst objects but they also do book-keeping: They remove unnecessary channels, or update their information and they ensure that three tables (signal, segments, and events) match. It's then not recommended to edit the signal and segments table directly. (The events and channels table can be edited directly by doing events_tbl(data_eeg) <- ... or channels_tbl(data_eeg) <- ...).
Other tidyverse-like functions:
drop_incomplete_segments(),
eeg_bind(),
eeg_slice_signal()
library(dplyr)
# Create new channel in the signal table
data_faces_ERPs %>%
eeg_mutate(tmp = Fz - Cz)
#> # EEG data:
#>
#> # Signal table:
#> .id .sample Fp1 Fpz Fp2 F7 F3
#> 1: 1 -99 0.22353428 0.6683971 -0.03621027 0.8601528 1.716951
#> 2: 1 -98 0.25257599 0.7361622 -0.04687375 0.8352839 1.696101
#> 3: 1 -97 0.13301252 0.7052292 -0.15697831 0.7692707 1.536542
#> 4: 1 -96 -0.08392458 0.6128529 -0.24638127 0.6806059 1.271216
#> 5: 1 -95 -0.28915086 0.5225633 -0.25209382 0.6288176 0.976661
#> ---
#> 448: 2 122 -13.17179185 -14.3696799 -13.51380498 -8.9363176 -12.706181
#> 449: 2 123 -13.51617769 -14.6111804 -13.53817011 -8.9676788 -12.858616
#> 450: 2 124 -13.45730441 -14.7025321 -13.46071115 -8.8665316 -12.906408
#> 451: 2 125 -13.29286410 -14.7043950 -13.39205897 -8.7878105 -12.838448
#> 452: 2 126 -13.22445326 -14.7907774 -13.53960969 -8.8170396 -12.767814
#> Fz F4 F8 FC5 FC1 FC2
#> 1: 0.6631765 -0.092648050 6.219736e-04 1.6053903 1.543430 0.6253292
#> 2: 0.6964380 0.007091334 -7.299999e-03 1.5366046 1.570444 0.6661446
#> 3: 0.6526393 0.011369770 -1.129255e-01 1.3769061 1.544950 0.6605502
#> 4: 0.5556773 -0.029683895 -2.124353e-01 1.1235654 1.454882 0.6033796
#> 5: 0.4588411 -0.091315813 -3.011045e-01 0.8645434 1.338942 0.5266072
#> ---
#> 448: -13.2277295 -14.096793251 -1.035504e+01 -7.9050573 -9.123321 -11.4783766
#> 449: -13.3456251 -14.190243051 -1.030666e+01 -8.2451572 -9.170633 -11.5308694
#> 450: -13.3703569 -14.177192252 -1.022589e+01 -8.3741325 -9.188665 -11.5363037
#> 451: -13.3490926 -14.130527072 -1.018281e+01 -8.2735300 -9.206411 -11.5181813
#> 452: -13.3761194 -14.161811583 -1.025209e+01 -8.1323938 -9.258206 -11.5542280
#> FC6 M1 T7 C3 Cz C4
#> 1: 0.3170046 -0.010442968 0.7618859 2.333914 0.9975520 1.934568
#> 2: 0.4931471 0.003450777 0.7753494 2.412899 1.0106905 1.954956
#> 3: 0.5423985 0.033199993 0.8647610 2.504287 0.9923178 1.936489
#> 4: 0.4838804 0.060024264 0.9365834 2.518237 0.9274194 1.874189
#> 5: 0.3697913 0.104232711 0.9420319 2.451159 0.8381762 1.789200
#> ---
#> 448: -10.0753595 -1.698556249 -2.2955267 -3.660581 -7.7390500 -7.136071
#> 449: -10.0855337 -1.601363953 -2.4438153 -3.674479 -7.7419611 -7.290376
#> 450: -10.0955933 -1.471049249 -2.5843704 -3.729771 -7.7226622 -7.399979
#> 451: -10.1111284 -1.316673196 -2.6855790 -3.866381 -7.7275323 -7.497261
#> 452: -10.1808181 -1.189507126 -2.7373231 -4.060195 -7.7703272 -7.634738
#> T8 M2 CP5 CP1 CP2 CP6 P7
#> 1: 0.1472479 0.010442968 1.429704 1.3638380 1.468067 1.41452662 1.054939
#> 2: 0.2699401 -0.003450777 1.494439 1.4090212 1.481492 1.45458898 1.116253
#> 3: 0.3409814 -0.033199993 1.616789 1.4711696 1.517598 1.48051308 1.353048
#> 4: 0.3652337 -0.060024264 1.688633 1.4957534 1.541262 1.43353669 1.573497
#> 5: 0.3596960 -0.104232711 1.712986 1.4788877 1.536297 1.33977245 1.710138
#> ---
#> 448: -4.0330479 1.698556249 5.591801 0.6443219 -2.855460 0.58107099 13.163428
#> 449: -3.9037055 1.601363953 5.522963 0.6451574 -2.885057 0.35450182 12.958382
#> 450: -3.9050442 1.471049249 5.467754 0.6242272 -2.863814 0.24704496 12.739427
#> 451: -4.1678197 1.316673196 5.445742 0.5686631 -2.874607 0.19361613 12.660220
#> 452: -4.5113966 1.189507126 5.437697 0.4595132 -2.963804 0.06697914 12.745258
#> P3 Pz P4 P8 POz O1 Oz
#> 1: 1.277304 1.144086 1.648778 0.4936695 0.9985673 0.8084641 0.6250410
#> 2: 1.285327 1.152601 1.671552 0.5009267 1.0276117 0.8976453 0.7248799
#> 3: 1.404817 1.202807 1.724722 0.5416201 1.1316823 1.1302819 0.9194691
#> 4: 1.529925 1.253876 1.762264 0.5757036 1.2251255 1.2880753 1.0329156
#> 5: 1.627608 1.292309 1.768303 0.6045430 1.2946317 1.3478098 1.0808014
#> ---
#> 448: 9.132918 2.897569 6.218040 10.6124393 7.8505588 8.3873355 7.4918637
#> 449: 9.087724 2.920497 6.107610 10.5269463 7.7910463 8.0510860 7.2997719
#> 450: 9.036488 2.945566 6.015894 10.3746554 7.7270142 7.7371189 7.0718979
#> 451: 9.010008 2.945949 5.923691 10.1381542 7.6785265 7.6067531 6.9113667
#> 452: 8.991352 2.911724 5.805668 9.8648190 7.6702641 7.6972328 6.9127040
#> O2 EOGV EOGH tmp
#> 1: 0.9080361 1.3865825 2.361059 -0.3343755
#> 2: 0.9601037 1.3008403 2.446400 -0.3142525
#> 3: 1.0825952 1.3182415 2.405026 -0.3396785
#> 4: 1.1729106 1.4509676 2.221037 -0.3717421
#> 5: 1.2469059 1.5167961 1.952367 -0.3793351
#> ---
#> 448: 12.4155999 0.8569236 6.530531 -5.4886796
#> 449: 12.3021953 0.5408142 6.170865 -5.6036640
#> 450: 12.0573028 0.1136463 5.719135 -5.6476947
#> 451: 11.7688274 -0.2998507 5.299783 -5.6215603
#> 452: 11.6048341 -0.4865012 5.040388 -5.6057921
#>
#> # Events table:
#> No events.
#>
#> # Segments table:
#> .id .recording condition
#> 1: 1 faces.vhdr faces
#> 2: 2 faces.vhdr non-faces
# Create a new condition in the segments table
data_faces_ERPs %>%
eeg_mutate(code = ifelse(condition == "faces", 1, -1))
#> # EEG data:
#>
#> # Signal table:
#> .id .sample Fp1 Fpz Fp2 F7 F3
#> 1: 1 -99 0.22353428 0.6683971 -0.03621027 0.8601528 1.716951
#> 2: 1 -98 0.25257599 0.7361622 -0.04687375 0.8352839 1.696101
#> 3: 1 -97 0.13301252 0.7052292 -0.15697831 0.7692707 1.536542
#> 4: 1 -96 -0.08392458 0.6128529 -0.24638127 0.6806059 1.271216
#> 5: 1 -95 -0.28915086 0.5225633 -0.25209382 0.6288176 0.976661
#> ---
#> 448: 2 122 -13.17179185 -14.3696799 -13.51380498 -8.9363176 -12.706181
#> 449: 2 123 -13.51617769 -14.6111804 -13.53817011 -8.9676788 -12.858616
#> 450: 2 124 -13.45730441 -14.7025321 -13.46071115 -8.8665316 -12.906408
#> 451: 2 125 -13.29286410 -14.7043950 -13.39205897 -8.7878105 -12.838448
#> 452: 2 126 -13.22445326 -14.7907774 -13.53960969 -8.8170396 -12.767814
#> Fz F4 F8 FC5 FC1 FC2
#> 1: 0.6631765 -0.092648050 6.219736e-04 1.6053903 1.543430 0.6253292
#> 2: 0.6964380 0.007091334 -7.299999e-03 1.5366046 1.570444 0.6661446
#> 3: 0.6526393 0.011369770 -1.129255e-01 1.3769061 1.544950 0.6605502
#> 4: 0.5556773 -0.029683895 -2.124353e-01 1.1235654 1.454882 0.6033796
#> 5: 0.4588411 -0.091315813 -3.011045e-01 0.8645434 1.338942 0.5266072
#> ---
#> 448: -13.2277295 -14.096793251 -1.035504e+01 -7.9050573 -9.123321 -11.4783766
#> 449: -13.3456251 -14.190243051 -1.030666e+01 -8.2451572 -9.170633 -11.5308694
#> 450: -13.3703569 -14.177192252 -1.022589e+01 -8.3741325 -9.188665 -11.5363037
#> 451: -13.3490926 -14.130527072 -1.018281e+01 -8.2735300 -9.206411 -11.5181813
#> 452: -13.3761194 -14.161811583 -1.025209e+01 -8.1323938 -9.258206 -11.5542280
#> FC6 M1 T7 C3 Cz C4
#> 1: 0.3170046 -0.010442968 0.7618859 2.333914 0.9975520 1.934568
#> 2: 0.4931471 0.003450777 0.7753494 2.412899 1.0106905 1.954956
#> 3: 0.5423985 0.033199993 0.8647610 2.504287 0.9923178 1.936489
#> 4: 0.4838804 0.060024264 0.9365834 2.518237 0.9274194 1.874189
#> 5: 0.3697913 0.104232711 0.9420319 2.451159 0.8381762 1.789200
#> ---
#> 448: -10.0753595 -1.698556249 -2.2955267 -3.660581 -7.7390500 -7.136071
#> 449: -10.0855337 -1.601363953 -2.4438153 -3.674479 -7.7419611 -7.290376
#> 450: -10.0955933 -1.471049249 -2.5843704 -3.729771 -7.7226622 -7.399979
#> 451: -10.1111284 -1.316673196 -2.6855790 -3.866381 -7.7275323 -7.497261
#> 452: -10.1808181 -1.189507126 -2.7373231 -4.060195 -7.7703272 -7.634738
#> T8 M2 CP5 CP1 CP2 CP6 P7
#> 1: 0.1472479 0.010442968 1.429704 1.3638380 1.468067 1.41452662 1.054939
#> 2: 0.2699401 -0.003450777 1.494439 1.4090212 1.481492 1.45458898 1.116253
#> 3: 0.3409814 -0.033199993 1.616789 1.4711696 1.517598 1.48051308 1.353048
#> 4: 0.3652337 -0.060024264 1.688633 1.4957534 1.541262 1.43353669 1.573497
#> 5: 0.3596960 -0.104232711 1.712986 1.4788877 1.536297 1.33977245 1.710138
#> ---
#> 448: -4.0330479 1.698556249 5.591801 0.6443219 -2.855460 0.58107099 13.163428
#> 449: -3.9037055 1.601363953 5.522963 0.6451574 -2.885057 0.35450182 12.958382
#> 450: -3.9050442 1.471049249 5.467754 0.6242272 -2.863814 0.24704496 12.739427
#> 451: -4.1678197 1.316673196 5.445742 0.5686631 -2.874607 0.19361613 12.660220
#> 452: -4.5113966 1.189507126 5.437697 0.4595132 -2.963804 0.06697914 12.745258
#> P3 Pz P4 P8 POz O1 Oz
#> 1: 1.277304 1.144086 1.648778 0.4936695 0.9985673 0.8084641 0.6250410
#> 2: 1.285327 1.152601 1.671552 0.5009267 1.0276117 0.8976453 0.7248799
#> 3: 1.404817 1.202807 1.724722 0.5416201 1.1316823 1.1302819 0.9194691
#> 4: 1.529925 1.253876 1.762264 0.5757036 1.2251255 1.2880753 1.0329156
#> 5: 1.627608 1.292309 1.768303 0.6045430 1.2946317 1.3478098 1.0808014
#> ---
#> 448: 9.132918 2.897569 6.218040 10.6124393 7.8505588 8.3873355 7.4918637
#> 449: 9.087724 2.920497 6.107610 10.5269463 7.7910463 8.0510860 7.2997719
#> 450: 9.036488 2.945566 6.015894 10.3746554 7.7270142 7.7371189 7.0718979
#> 451: 9.010008 2.945949 5.923691 10.1381542 7.6785265 7.6067531 6.9113667
#> 452: 8.991352 2.911724 5.805668 9.8648190 7.6702641 7.6972328 6.9127040
#> O2 EOGV EOGH
#> 1: 0.9080361 1.3865825 2.361059
#> 2: 0.9601037 1.3008403 2.446400
#> 3: 1.0825952 1.3182415 2.405026
#> 4: 1.1729106 1.4509676 2.221037
#> 5: 1.2469059 1.5167961 1.952367
#> ---
#> 448: 12.4155999 0.8569236 6.530531
#> 449: 12.3021953 0.5408142 6.170865
#> 450: 12.0573028 0.1136463 5.719135
#> 451: 11.7688274 -0.2998507 5.299783
#> 452: 11.6048341 -0.4865012 5.040388
#>
#> # Events table:
#> No events.
#>
#> # Segments table:
#> .id .recording condition code
#> 1: 1 faces.vhdr faces 1
#> 2: 2 faces.vhdr non-faces -1
# Create a new channel and drop all others
data_faces_ERPs %>%
eeg_transmute(Occipital = chs_mean(O1, O2, Oz,
na.rm = TRUE
))
#> # EEG data:
#>
#> # Signal table:
#> .id .sample Occipital
#> 1: 1 -99 0.7805137
#> 2: 1 -98 0.8608763
#> 3: 1 -97 1.0441154
#> 4: 1 -96 1.1646338
#> 5: 1 -95 1.2251724
#> ---
#> 448: 2 122 9.4315997
#> 449: 2 123 9.2176844
#> 450: 2 124 8.9554399
#> 451: 2 125 8.7623157
#> 452: 2 126 8.7382569
#>
#> # Events table:
#> No events.
#>
#> # Segments table:
#> .id .recording condition
#> 1: 1 faces.vhdr faces
#> 2: 2 faces.vhdr non-faces
# Extract data associated with a condition
data_faces_ERPs %>%
eeg_filter(condition == "faces")
#> # EEG data:
#>
#> # Signal table:
#> .id .sample Fp1 Fpz Fp2 F7 F3
#> 1: 1 -99 0.22353428 0.6683971 -0.03621027 0.8601528 1.716951
#> 2: 1 -98 0.25257599 0.7361622 -0.04687375 0.8352839 1.696101
#> 3: 1 -97 0.13301252 0.7052292 -0.15697831 0.7692707 1.536542
#> 4: 1 -96 -0.08392458 0.6128529 -0.24638127 0.6806059 1.271216
#> 5: 1 -95 -0.28915086 0.5225633 -0.25209382 0.6288176 0.976661
#> ---
#> 222: 1 122 -2.94918210 -4.5101606 -5.06047275 -2.9873033 -5.465723
#> 223: 1 123 -3.27351815 -4.7767685 -5.31172654 -3.0590449 -5.650225
#> 224: 1 124 -3.60232770 -5.0461123 -5.61465058 -3.1751720 -5.871726
#> 225: 1 125 -3.72426044 -5.1661769 -5.73948259 -3.2380343 -5.978774
#> 226: 1 126 -3.52445504 -5.0158643 -5.59983333 -3.1634585 -5.942431
#> Fz F4 F8 FC5 FC1 FC2
#> 1: 0.6631765 -0.092648050 0.0006219736 1.6053903 1.543430 0.6253292
#> 2: 0.6964380 0.007091334 -0.0072999994 1.5366046 1.570444 0.6661446
#> 3: 0.6526393 0.011369770 -0.1129255121 1.3769061 1.544950 0.6605502
#> 4: 0.5556773 -0.029683895 -0.2124352841 1.1235654 1.454882 0.6033796
#> 5: 0.4588411 -0.091315813 -0.3011045087 0.8645434 1.338942 0.5266072
#> ---
#> 222: -5.9787921 -7.268414620 -5.0495993187 -3.3844155 -4.549848 -5.3434164
#> 223: -6.1201041 -7.364111335 -5.0456244674 -3.3672154 -4.751060 -5.4377672
#> 224: -6.2458631 -7.463538062 -5.1291662134 -3.5245886 -4.966285 -5.5437213
#> 225: -6.3001693 -7.480928252 -5.1644797151 -3.6780080 -5.102491 -5.5923294
#> 226: -6.2329611 -7.361764714 -5.0965870591 -3.6977668 -5.121900 -5.5163632
#> FC6 M1 T7 C3 Cz C4
#> 1: 0.3170046 -0.010442968 0.7618859 2.333914 0.9975520 1.934568
#> 2: 0.4931471 0.003450777 0.7753494 2.412899 1.0106905 1.954956
#> 3: 0.5423985 0.033199993 0.8647610 2.504287 0.9923178 1.936489
#> 4: 0.4838804 0.060024264 0.9365834 2.518237 0.9274194 1.874189
#> 5: 0.3697913 0.104232711 0.9420319 2.451159 0.8381762 1.789200
#> ---
#> 222: -5.1618290 -1.652771628 -1.7384178 -1.721975 -3.0634028 -2.730119
#> 223: -5.3257419 -1.583403006 -1.7830883 -1.967438 -3.2382530 -3.015366
#> 224: -5.5280940 -1.521513044 -1.8792932 -2.246942 -3.4281573 -3.263800
#> 225: -5.6791349 -1.444548047 -1.9536678 -2.484138 -3.5731374 -3.404891
#> 226: -5.6552421 -1.359428020 -1.9701087 -2.628690 -3.6230233 -3.399993
#> T8 M2 CP5 CP1 CP2 CP6 P7
#> 1: 0.1472479 0.010442968 1.429704 1.3638380 1.4680673 1.4145266 1.054939
#> 2: 0.2699401 -0.003450777 1.494439 1.4090212 1.4814922 1.4545890 1.116253
#> 3: 0.3409814 -0.033199993 1.616789 1.4711696 1.5175983 1.4805131 1.353048
#> 4: 0.3652337 -0.060024264 1.688633 1.4957534 1.5412618 1.4335367 1.573497
#> 5: 0.3596960 -0.104232711 1.712986 1.4788877 1.5362970 1.3397724 1.710138
#> ---
#> 222: -1.4827466 1.652771628 2.520838 1.6828339 0.8020536 2.3775401 5.017926
#> 223: -1.5070728 1.583403006 2.285606 1.3649900 0.4781533 1.7982843 4.849579
#> 224: -1.7046259 1.521513044 2.050171 1.0174482 0.1467149 1.3049149 4.750217
#> 225: -1.9110615 1.444548047 1.859121 0.7112496 -0.1275205 1.0217818 4.692425
#> 226: -2.0251590 1.359428020 1.722453 0.4969751 -0.2703340 0.9117485 4.607363
#> P3 Pz P4 P8 POz O1 Oz
#> 1: 1.277304 1.144086 1.648778 0.4936695 0.9985673 0.8084641 0.6250410
#> 2: 1.285327 1.152601 1.671552 0.5009267 1.0276117 0.8976453 0.7248799
#> 3: 1.404817 1.202807 1.724722 0.5416201 1.1316823 1.1302819 0.9194691
#> 4: 1.529925 1.253876 1.762264 0.5757036 1.2251255 1.2880753 1.0329156
#> 5: 1.627608 1.292309 1.768303 0.6045430 1.2946317 1.3478098 1.0808014
#> ---
#> 222: 6.429410 4.410422 7.335071 7.9385283 7.2843393 4.4533680 5.1915785
#> 223: 6.070601 4.052613 6.900739 7.5721019 6.8981251 4.2380301 4.9148969
#> 224: 5.714930 3.677770 6.475128 7.2035631 6.5300319 4.1816044 4.7546588
#> 225: 5.412841 3.352800 6.124055 6.8945005 6.2105246 4.1942757 4.6067733
#> 226: 5.189596 3.133808 5.883263 6.6380102 5.9589774 4.1527416 4.3954874
#> O2 EOGV EOGH
#> 1: 0.9080361 1.386582 2.361059
#> 2: 0.9601037 1.300840 2.446400
#> 3: 1.0825952 1.318241 2.405026
#> 4: 1.1729106 1.450968 2.221037
#> 5: 1.2469059 1.516796 1.952367
#> ---
#> 222: 9.9765440 4.875008 8.624626
#> 223: 9.6052505 4.764172 8.413440
#> 224: 9.2860807 4.667508 8.234419
#> 225: 8.9578564 4.484040 8.085010
#> 226: 8.5663634 4.178376 7.937325
#>
#> # Events table:
#> No events.
#>
#> # Segments table:
#> .id .recording condition
#> 1: 1 faces.vhdr faces
# Group and summarize
data_faces_ERPs %>%
# Convert samples to times, filter between timepoints
eeg_filter(between(
as_time(.sample, .unit = "ms"),
100, 200
)) %>%
# Find mean amplitude of Fz for each condition
eeg_group_by(condition) %>%
eeg_summarize(mean.amplitude = mean(Fz))
#> # EEG data:
#> # Grouped by: condition,
#>
#> # Signal table:
#> .id .sample mean.amplitude
#> 1: 1 NA -1.123384
#> 2: 2 NA -5.701296
#>
#> # Events table:
#> No events.
#>
#> # Segments table:
#> .id .recording condition
#> 1: 1 <NA> faces
#> 2: 2 <NA> non-faces
# Mean of each channel
data_faces_ERPs %>%
eeg_summarize(across_ch(mean))
#> # EEG data:
#>
#> # Signal table:
#> .id .sample Fp1 Fpz Fp2 F7 F3 Fz
#> 1: 1 NA -1.332362 -1.567974 -1.821664 -1.064499 -1.752809 -1.842959
#> F4 F8 FC5 FC1 FC2 FC6 M1
#> 1: -1.998877 -1.507871 -1.111193 -1.444029 -1.650133 -1.524692 -0.2682345
#> T7 C3 Cz C4 T8 M2 CP5
#> 1: -0.3089361 -0.7513462 -1.146025 -1.147417 -0.764417 0.2682345 0.8355049
#> CP1 CP2 CP6 P7 P3 Pz P4 P8
#> 1: 0.1305391 -0.2710436 0.01620356 2.14035 1.800169 0.8448844 1.22103 1.378821
#> POz O1 Oz O2 EOGV EOGH
#> 1: 1.695645 1.526005 1.274255 2.30676 0.6111195 0.9612286
#>
#> # Events table:
#> No events.
#>
#> # Segments table:
#> .id .recording
#> 1: 1 <NA>
# Select specific electrodes
data_faces_ERPs %>%
eeg_select(O1, O2, P7, P8)
#> # EEG data:
#>
#> # Signal table:
#> .id .sample O1 O2 P7 P8
#> 1: 1 -99 0.8084641 0.9080361 1.054939 0.4936695
#> 2: 1 -98 0.8976453 0.9601037 1.116253 0.5009267
#> 3: 1 -97 1.1302819 1.0825952 1.353048 0.5416201
#> 4: 1 -96 1.2880753 1.1729106 1.573497 0.5757036
#> 5: 1 -95 1.3478098 1.2469059 1.710138 0.6045430
#> ---
#> 448: 2 122 8.3873355 12.4155999 13.163428 10.6124393
#> 449: 2 123 8.0510860 12.3021953 12.958382 10.5269463
#> 450: 2 124 7.7371189 12.0573028 12.739427 10.3746554
#> 451: 2 125 7.6067531 11.7688274 12.660220 10.1381542
#> 452: 2 126 7.6972328 11.6048341 12.745258 9.8648190
#>
#> # Events table:
#> No events.
#>
#> # Segments table:
#> .id .recording condition
#> 1: 1 faces.vhdr faces
#> 2: 2 faces.vhdr non-faces
# Rename a variable
data_faces_ERPs %>%
eeg_rename(Predictor = condition)
#> # EEG data:
#>
#> # Signal table:
#> .id .sample Fp1 Fpz Fp2 F7 F3
#> 1: 1 -99 0.22353428 0.6683971 -0.03621027 0.8601528 1.716951
#> 2: 1 -98 0.25257599 0.7361622 -0.04687375 0.8352839 1.696101
#> 3: 1 -97 0.13301252 0.7052292 -0.15697831 0.7692707 1.536542
#> 4: 1 -96 -0.08392458 0.6128529 -0.24638127 0.6806059 1.271216
#> 5: 1 -95 -0.28915086 0.5225633 -0.25209382 0.6288176 0.976661
#> ---
#> 448: 2 122 -13.17179185 -14.3696799 -13.51380498 -8.9363176 -12.706181
#> 449: 2 123 -13.51617769 -14.6111804 -13.53817011 -8.9676788 -12.858616
#> 450: 2 124 -13.45730441 -14.7025321 -13.46071115 -8.8665316 -12.906408
#> 451: 2 125 -13.29286410 -14.7043950 -13.39205897 -8.7878105 -12.838448
#> 452: 2 126 -13.22445326 -14.7907774 -13.53960969 -8.8170396 -12.767814
#> Fz F4 F8 FC5 FC1 FC2
#> 1: 0.6631765 -0.092648050 6.219736e-04 1.6053903 1.543430 0.6253292
#> 2: 0.6964380 0.007091334 -7.299999e-03 1.5366046 1.570444 0.6661446
#> 3: 0.6526393 0.011369770 -1.129255e-01 1.3769061 1.544950 0.6605502
#> 4: 0.5556773 -0.029683895 -2.124353e-01 1.1235654 1.454882 0.6033796
#> 5: 0.4588411 -0.091315813 -3.011045e-01 0.8645434 1.338942 0.5266072
#> ---
#> 448: -13.2277295 -14.096793251 -1.035504e+01 -7.9050573 -9.123321 -11.4783766
#> 449: -13.3456251 -14.190243051 -1.030666e+01 -8.2451572 -9.170633 -11.5308694
#> 450: -13.3703569 -14.177192252 -1.022589e+01 -8.3741325 -9.188665 -11.5363037
#> 451: -13.3490926 -14.130527072 -1.018281e+01 -8.2735300 -9.206411 -11.5181813
#> 452: -13.3761194 -14.161811583 -1.025209e+01 -8.1323938 -9.258206 -11.5542280
#> FC6 M1 T7 C3 Cz C4
#> 1: 0.3170046 -0.010442968 0.7618859 2.333914 0.9975520 1.934568
#> 2: 0.4931471 0.003450777 0.7753494 2.412899 1.0106905 1.954956
#> 3: 0.5423985 0.033199993 0.8647610 2.504287 0.9923178 1.936489
#> 4: 0.4838804 0.060024264 0.9365834 2.518237 0.9274194 1.874189
#> 5: 0.3697913 0.104232711 0.9420319 2.451159 0.8381762 1.789200
#> ---
#> 448: -10.0753595 -1.698556249 -2.2955267 -3.660581 -7.7390500 -7.136071
#> 449: -10.0855337 -1.601363953 -2.4438153 -3.674479 -7.7419611 -7.290376
#> 450: -10.0955933 -1.471049249 -2.5843704 -3.729771 -7.7226622 -7.399979
#> 451: -10.1111284 -1.316673196 -2.6855790 -3.866381 -7.7275323 -7.497261
#> 452: -10.1808181 -1.189507126 -2.7373231 -4.060195 -7.7703272 -7.634738
#> T8 M2 CP5 CP1 CP2 CP6 P7
#> 1: 0.1472479 0.010442968 1.429704 1.3638380 1.468067 1.41452662 1.054939
#> 2: 0.2699401 -0.003450777 1.494439 1.4090212 1.481492 1.45458898 1.116253
#> 3: 0.3409814 -0.033199993 1.616789 1.4711696 1.517598 1.48051308 1.353048
#> 4: 0.3652337 -0.060024264 1.688633 1.4957534 1.541262 1.43353669 1.573497
#> 5: 0.3596960 -0.104232711 1.712986 1.4788877 1.536297 1.33977245 1.710138
#> ---
#> 448: -4.0330479 1.698556249 5.591801 0.6443219 -2.855460 0.58107099 13.163428
#> 449: -3.9037055 1.601363953 5.522963 0.6451574 -2.885057 0.35450182 12.958382
#> 450: -3.9050442 1.471049249 5.467754 0.6242272 -2.863814 0.24704496 12.739427
#> 451: -4.1678197 1.316673196 5.445742 0.5686631 -2.874607 0.19361613 12.660220
#> 452: -4.5113966 1.189507126 5.437697 0.4595132 -2.963804 0.06697914 12.745258
#> P3 Pz P4 P8 POz O1 Oz
#> 1: 1.277304 1.144086 1.648778 0.4936695 0.9985673 0.8084641 0.6250410
#> 2: 1.285327 1.152601 1.671552 0.5009267 1.0276117 0.8976453 0.7248799
#> 3: 1.404817 1.202807 1.724722 0.5416201 1.1316823 1.1302819 0.9194691
#> 4: 1.529925 1.253876 1.762264 0.5757036 1.2251255 1.2880753 1.0329156
#> 5: 1.627608 1.292309 1.768303 0.6045430 1.2946317 1.3478098 1.0808014
#> ---
#> 448: 9.132918 2.897569 6.218040 10.6124393 7.8505588 8.3873355 7.4918637
#> 449: 9.087724 2.920497 6.107610 10.5269463 7.7910463 8.0510860 7.2997719
#> 450: 9.036488 2.945566 6.015894 10.3746554 7.7270142 7.7371189 7.0718979
#> 451: 9.010008 2.945949 5.923691 10.1381542 7.6785265 7.6067531 6.9113667
#> 452: 8.991352 2.911724 5.805668 9.8648190 7.6702641 7.6972328 6.9127040
#> O2 EOGV EOGH
#> 1: 0.9080361 1.3865825 2.361059
#> 2: 0.9601037 1.3008403 2.446400
#> 3: 1.0825952 1.3182415 2.405026
#> 4: 1.1729106 1.4509676 2.221037
#> 5: 1.2469059 1.5167961 1.952367
#> ---
#> 448: 12.4155999 0.8569236 6.530531
#> 449: 12.3021953 0.5408142 6.170865
#> 450: 12.0573028 0.1136463 5.719135
#> 451: 11.7688274 -0.2998507 5.299783
#> 452: 11.6048341 -0.4865012 5.040388
#>
#> # Events table:
#> No events.
#>
#> # Segments table:
#> .id .recording Predictor
#> 1: 1 faces.vhdr faces
#> 2: 2 faces.vhdr non-faces