,

This function returns an extended eeg_lst, eeg_ica_lst, with the mixing and unmixing matrix of every recording. It is possible to visualize the topography of the components with plot_components(). In order to extract the amplitude of the components with respect to time use eeg_ica_show(), see examples section. To remove the unwanted components, use eeg_ica_keep().

eeg_ica(
  .data,
  ...,
  .ignore = .type == "artifact",
  .method = fast_ICA,
  .config = list()
)

Arguments

.data

An eeg_lst object

...

Channels to include in ICA transformation. All the channels by default, but eye channels and reference channels should be removed.

.ignore

Events that should be ignored in the ICA, set to NULL for not using the events table.

.method

Methods from different packages: fast_ICA, a wrapper of fastICA::fastICA, (default), and some more experimental .methods: fast_ICA2 and adapt_fast_ICA adapted from fICA package. It can also accept a custom function, see details.

.config

Other parameters passed in a list to the ICA method. See the documentation of the relevant .method.

Value

An eeg_ica_lst object

Details

It is possible to also use a custom function in the method argument. The function should return a list that with A (mixing matrix), consistent with the formulation X = S %*% A, where X is matrix of N_samples by N_channels and/or W (unmixing matrix), consistent with the formulation X %*% W = S. Some packages with other ICA methods or implementations: steadyICA and ica.

See also

Examples

# For demonstration only, since ICA won't converge
# Suppressing an important warning:
suppressWarnings(data_faces_10_trials %>%
  eeg_ica(-EOGH, -EOGV, -M1, -M2, .method = fast_ICA, .config = list(maxit = 10)))
#> # ICA is being done using fast_ICA...
#> # ICA finished in 9 iterations. With a tolerance of 0.991338. (Maximum expected tolerance: 1e-06.)
#> # ICA took 1.05 secs
#> # EEG data:
#> 
#> # Signal table:
#> Key: <.id, .sample>
#>          .id      .sample           Fp1           Fpz           Fp2
#>        <int> <sample_int> <channel_dbl> <channel_dbl> <channel_dbl>
#>     1:     1        15000     -75.03562     -28.57522    -28.165712
#>     2:     1        15001     -65.61619     -33.53533    -24.389921
#>     3:     1        15002     -53.28476     -33.01109    -16.815264
#>     4:     1        15003     -49.85156     -31.56947    -12.792553
#>     5:     1        15004     -51.95777     -30.04367    -11.117691
#>    ---                                                             
#> 43325:     1        58324     -20.42674     -14.87641    -10.669024
#> 43326:     1        58325     -19.79967     -13.65431     -9.698612
#> 43327:     1        58326     -18.61094     -13.91012    -10.128812
#> 43328:     1        58327     -16.96312     -14.78610    -11.301287
#> 43329:     1        58328     -15.31518     -15.08285    -11.722956
#>                   F7            F3            Fz            F4            F8
#>        <channel_dbl> <channel_dbl> <channel_dbl> <channel_dbl> <channel_dbl>
#>     1:   -35.1809807    -29.561008    -11.504043     -4.137532      9.616964
#>     2:   -24.2422733    -26.622681    -13.667097     -6.835285     11.225069
#>     3:   -11.4339190    -19.439884    -13.724218     -7.711213     10.861008
#>     4:    -6.6371970    -16.500380    -13.453588     -7.477996      9.886633
#>     5:    -7.1528592    -15.707507    -12.726236     -6.223224     10.369526
#>    ---                                                                      
#> 43325:    -0.5581126    -10.197008     -8.468801     -5.040645     -9.132243
#> 43326:    -0.6716754     -8.732699     -7.252934     -3.377731     -8.813243
#> 43327:    -0.7749598     -8.517799     -6.654031     -2.315117     -7.843120
#> 43328:    -0.7414806     -9.084275     -6.622538     -1.845731     -6.556810
#> 43329:    -0.3960888     -9.219618     -6.436240     -1.520404     -5.406781
#>                  FC5           FC1           FC2           FC6            M1
#>        <channel_dbl> <channel_dbl> <channel_dbl> <channel_dbl> <channel_dbl>
#>     1:    -13.626797   -16.7902756     -4.733483   20.05954933    -1.8668146
#>     2:     -9.239326   -15.9672518     -8.509321    4.00367641    -3.2960243
#>     3:    -10.491687   -11.6829367    -10.936778   -5.81625843    -3.7450671
#>     4:    -13.772001    -9.7923832    -11.740524   -5.40025806    -3.7946916
#>     5:    -11.491154    -9.8350058    -10.939524   -0.02480793    -3.2719884
#>    ---                                                                      
#> 43325:     -7.535339    -2.3418787     -3.478385   -9.38459492     0.8050708
#> 43326:     -9.061241    -1.0433049     -1.930508   -7.47599792     0.9832335
#> 43327:     -7.603340    -0.6127751     -1.379790   -5.71040392     1.2051768
#> 43328:     -2.782797    -1.4022492     -1.640656   -4.64086485     1.4103985
#> 43329:      2.897767    -2.5480108     -1.864611   -4.35131598     1.1627738
#>                   T7            C3            Cz            C4            T8
#>        <channel_dbl> <channel_dbl> <channel_dbl> <channel_dbl> <channel_dbl>
#>     1: -24.323915482   -16.1792583    -17.846788     7.3229218    -16.999569
#>     2: -15.526218414   -14.2676773    -16.833292     2.9823387      1.561563
#>     3:  -9.126789093    -6.4200578    -14.397903     0.2715862     16.691229
#>     4: -12.452255249    -1.2270993    -14.878461     0.3950298     16.609812
#>     5: -17.832372665    -1.3789420    -16.662720     1.3136983      9.947195
#>    ---                                                                      
#> 43325:   0.003161311    -0.1895013     -4.354355    -8.3621836     -5.082950
#> 43326:   0.534484148     1.4632750     -2.513818    -6.4180498     -5.036983
#> 43327:   0.933975220     1.7615180     -1.322806    -5.1856117     -1.183164
#> 43328:   1.373799086    -0.1813622     -1.508159    -5.1739321      3.655909
#> 43329:   1.512201071    -3.2800312     -2.576048    -5.2422166      5.829760
#>                   M2           CP5           CP1           CP2           CP6
#>        <channel_dbl> <channel_dbl> <channel_dbl> <channel_dbl> <channel_dbl>
#>     1:     1.8668146    -5.6213522   -19.5446644    -10.161226      9.255368
#>     2:     3.2960243    -5.7683177   -18.2775211     -9.952282     12.423758
#>     3:     3.7450671    -1.5748284   -14.0693884     -7.703567     16.022276
#>     4:     3.7946916     3.9857802   -12.2347841     -7.086234     18.729359
#>     5:     3.2719884     6.8450770   -13.1421146     -8.301058     16.974117
#>    ---                                                                      
#> 43325:    -0.8050708     3.3321576     0.7318851     -8.517914    -14.386089
#> 43326:    -0.9832335     3.6177154     2.1529536     -6.548975    -11.468288
#> 43327:    -1.2051768     3.1916180     2.6771717     -5.318815     -8.818744
#> 43328:    -1.4103985     1.9870222     1.8302985     -5.515146     -7.753020
#> 43329:    -1.1627738     0.5995106     0.1455368     -6.459312     -7.319424
#>                   P7            P3            Pz            P4            P8
#>        <channel_dbl> <channel_dbl> <channel_dbl> <channel_dbl> <channel_dbl>
#>     1:    -2.1418185    -7.6355362    -12.714437     -5.254761   -10.3836517
#>     2:    -1.8970445    -8.8778076    -12.883518     -4.029041    -4.5240870
#>     3:     0.5571944    -6.6042719    -11.049878     -1.706636     0.7570043
#>     4:     3.3928101    -3.9107032    -10.942114     -1.011510     1.3604219
#>     5:     4.5437369    -3.4414954    -12.527228     -2.947548    -2.2667072
#>    ---                                                                      
#> 43325:     7.0251837     0.5382314     -4.589949    -10.580815   -11.0312319
#> 43326:     6.4313159     0.9306827     -3.111303     -8.619126    -9.3256149
#> 43327:     4.4492149     0.3993354     -2.119561     -6.606555    -6.3650703
#> 43328:     2.3532932    -1.1857104     -2.376006     -5.663703    -4.0656471
#> 43329:     1.0884135    -3.2549555     -3.662545     -5.691663    -3.3207316
#>                  POz            O1            Oz            O2          EOGV
#>        <channel_dbl> <channel_dbl> <channel_dbl> <channel_dbl> <channel_dbl>
#>     1:    -14.801150   -13.6920586    -16.631485   -15.5945311    -205.85693
#>     2:    -13.380352   -10.6821909    -12.549113   -11.1155939    -214.15259
#>     3:    -10.860615    -7.1099706     -8.650514    -7.0801344    -220.57965
#>     4:    -10.532288    -5.4831371     -8.433714    -7.1516109    -218.24934
#>     5:    -12.728923    -7.6384282    -12.844979   -11.7087927    -217.16670
#>    ---                                                                      
#> 43325:     -5.136292     1.4443915     -5.164615    -8.2288170      18.41424
#> 43326:     -3.689569     1.4091530     -3.915735    -6.4314718      19.22052
#> 43327:     -2.319134     0.5369543     -2.351812    -3.4845843      19.74028
#> 43328:     -1.927569    -0.5624531     -1.339175    -1.1418194      19.20309
#> 43329:     -2.687999    -1.2014523     -1.284558    -0.7185705      18.72752
#>                 EOGH
#>        <channel_dbl>
#>     1:    164.320831
#>     2:    158.389755
#>     3:    157.559784
#>     4:    162.165161
#>     5:    168.559723
#>    ---              
#> 43325:      6.434383
#> 43326:      7.837689
#> 43327:      8.659687
#> 43328:      8.609533
#> 43329:      8.829977
#> 
#> ## Events table:
#>        .id        .type .description     .initial       .final .channel
#>      <int>       <char>       <char> <sample_int> <sample_int>   <char>
#>   1:     1 Bad Interval  Bad Min-Max        15000        15128      FC6
#>   2:     1 Bad Interval  Bad Min-Max        15000        15146       F8
#>   3:     1 Bad Interval  Bad Min-Max        15000        15142      Fp1
#>   4:     1 Bad Interval  Bad Min-Max        15000        15132      Fp2
#>   5:     1 Bad Interval  Bad Min-Max        15000        15140      Fpz
#>  ---                                                                   
#> 540:     1 Bad Interval  Bad Min-Max        55058        55400      Fpz
#> 541:     1 Bad Interval  Bad Min-Max        55077        55314       F8
#> 542:     1     Stimulus         s130        56380        56380     <NA>
#> 543:     1     Stimulus          s70        56750        56750     <NA>
#> 544:     1     Stimulus         s130        58328        58328     <NA>
#> 
#> # ICA:
#> # Component_names:ICA1, ICA2, ICA3, ICA4, ICA5, ICA6, ICA7, ICA8, ICA9, ICA10, ICA11, ICA12, ICA13, ICA14, ICA15, ICA16, ICA17, ICA18, ICA19, ICA20, ICA21, ICA22, ICA23, ICA24, ICA25, ICA26, ICA27, ICA28, ICA29, ICA30
#> # Channels_used: Fp1, Fpz, Fp2, F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, T7, C3, Cz, C4, T8, CP5, CP1, CP2, CP6, P7, P3, Pz, P4, P8, POz, O1, Oz, O2
#> 
#> # Segments table:
#> Key: <.id>
#>      .id .recording segment
#>    <int>     <char>   <int>
#> 1:     1 faces.vhdr       1

## The example can only bu run, if python is properly configured (see reticulate package help)
## Here a python ICA function is used:
if (FALSE) {
library(reticulate)
use_condaenv("anaconda3") # use the appropriate environment
sk <- import("sklearn.decomposition")
py_fica <- function(x) {
  x <- as.matrix(x)
  ica <- sk$FastICA(whiten = TRUE, random_state = 23L)
  X <- scale(x, scale = FALSE) %>%
    as.matrix(x)
  S <- ica$fit_transform(X)
  W <- t(ica$components_)
  list(W = W)
}
data_ica_py <- eeg_ica(data_faces_10_trials, -EOGH, -EOGV, -M1, -M2, .method = py_fica)
}