Description Usage Arguments Details Value Author(s) References See Also Examples
By-trial correction of EEG/MEG data for known (i.e., recorded) and unknown (i.e., not recorded) sources of noise.
1 2 3 4 |
x |
A data frame containing the EEG/MEG data to be corrected.
Measurements for each channel/electrode should be arranged in columns
with the channel/electrode to which it pertains as their names (e.g.,
|
channel |
The channels to correct. You can use the output of function
|
noise.sig |
The channel(s) against which each independent component (IC) will be correlated. Can be anything really, HEOG, VEOG, ECG, ... |
trial.cn |
The name of the column containing trial information. Defaults to "Trial". |
include |
Whether to include the noise channels in the ICA. Defaults to
|
threshold |
The correlation threshold between noise signal(s) and IC above which the EEG/MEG data will be corrected. Default is 0.4 (as in Flexer et al., 2005). Can be set to anything between 0 (will zero-out every IC) and 1 (will most probably zero-out nothing). |
n.comp |
Number of components. Defaults to the number of channels used. |
ica.method |
If |
correct |
Logical. Defaults to |
ica.only |
Logical. Defaults to |
proctime |
Logical. Defaults to |
seed |
Defaults to |
verbosity |
Numeric. The amount of information printed to screen during the modeling process. The higher the number, the more information is printed. |
... |
Further arguments to pass to function |
If the verbosity level is high enough, the output will contain the noise signal beeing processed, the trial, the IC, and the correlation between the noise signal and the IC at that trial. For example:
1 2 3 4 5 6 7 8 9 10 11 | ...
noise signal = Temp; trial = 19; IC = 6; cor = -0.307971687318979
noise signal = Temp; trial = 19; IC = 7; cor = 0.111036533642789
noise signal = Temp; trial = 19; IC = 8; cor = -0.0226991408620133
noise signal = Temp; trial = 19; IC = 9; cor = 0.233890667361682
noise signal = Temp; trial = 19; IC = 10; cor = 0.878635491834294
noise signal = Temp; trial = 19; IC = 11; cor = 0.0891185123593569
noise signal = Temp; trial = 19; IC = 12; cor = 0.524880913590867
noise signal = Temp; trial = 19; IC = 13; cor = -0.126156352285347
noise signal = Temp; trial = 19; IC = 14; cor = -0.312246072685998
...
|
If one wishes to simply know what ICs correlate at or above threshold with what
noise signal at what trial (i.e., no correction), set correct = FALSE
.
This would be done if one only wished to zero-out entire ICs without
zeroing-out anything else.
data |
If |
channel |
The channels that were corrected. |
noise.sig |
The noise signals for which the data were corrected. |
threshold |
The correlation threshold above which the EEG/MEG data will be corrected. |
n.comp |
The number of independent components used in the ICA. |
X |
Pre-processed data. |
K |
Pre-whitening matrix that projects data onto th first n.comp principal components. |
W |
The estimated un-mixing matrix. |
A |
The estimated mixing matrix. |
S |
If |
S0 |
The uncorrected estimated source matrix. |
col.means |
The mean of each channel. |
correlations |
For each noise signal and each trial, the correlation between the IC and the noise signal. |
correction.info |
A data frame with columns "NoiseSignal" (the noise signal with which ICs were compared), "IC" (the IC which correlated above threshold with the noise signal), "Trial" (the trial at which the noise signal and the IC correlated above threshold), and "Corr" (the correlation between the noise signal and the IC). |
proctime |
If |
Antoine Tremblay, Dalhousie University, trea26@gmail.com
Flexer, A., Bauer, H., Pripfl, J. & Dorffner, G. (2005). Using ICA for removal of ocular artifacts in EEG recorded from blind subjects. Neural Networks, 18, 998–1005.
Hyvarinen, Aapo & Oja, Erkki. (1999). Independent Component Analysis: A Tutorial. Available at http://cis.legacy.ics.tkk.fi/aapo/papers/IJCNN99_tutorialweb/.
fastICA
;
mwd.thrsh
;
plot_avgba
;
plot_trba
;
plot_nic
;
plot_tric
;
summary.icac
;
topo_ic
;
update.icac
.
1 | ### See vignette for examples.
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