Code
cat('\014') # clean terminalCode
rm(list = ls()) # clean workspace
library(tidyverse)
library(afex)
library(lmerTest)
library(emmeans)
library(easystats)ERPs Pain Empathy 2023, stationary device, 2 versions
Recordings with BioSemi ActiveTwo, 64 channels
Code
my_dodge <- .3
my_jitter <- .2
theme_set(theme_minimal())
a_posteriori_aov_ez <- function(aov_ez_object, sig_level = .05) {
factors <- as.list(rownames(aov_ez_object$anova_table))
for (j in 1:length(factors)) {
if (grepl(':', factors[[j]])) {
factors[[j]] <- unlist(strsplit(factors[[j]], ':'))
}
}
p_values <- aov_ez_object$anova_table$`Pr(>F)`
for (i in 1:length(p_values)) {
if (p_values[i] <= sig_level) {
cat(rep('_', 60), '\n', sep = '')
print(emmeans(aov_ez_object, factors[[i]], contr = 'pairwise'))
}
}
}
a_posteriori_lmer <- function(lmer_obj, sig_level = .05) {
anova_lmer <- anova(lmer_obj)
factors <- as.list(row.names(anova_lmer))
for (j in 1:length(factors)) {
if (grepl(':', factors[[j]])) {
factors[[j]] <- unlist(strsplit(factors[[j]], ':'))
}
}
p_values <- anova_lmer$`Pr(>F)`
for (i in 1:length(p_values)) {
if (p_values[i] <= sig_level) {
cat(rep('_', 60), '\n', sep = '')
print(emmeans(lmer_obj, factors[[i]], contr = 'pairwise'))
}
}
}
xclude <- c(666)Code
csv_answers <- list.files(path = '../csv', full.names = TRUE)
answers_df <- vroom::vroom(csv_answers, col_types = c(Sex = 'c'), show_col_types = FALSE) |>
filter(Block > 0) |>
mutate(Block = factor(Block)) |>
filter(!is.na(Question)) |>
mutate(Sex = if_else(Sex == 'f', 'female', 'male')) |>
separate(Subject, c('ID', 'Version'), sep = '_', remove = FALSE, extra = 'drop') |>
mutate(Version = tolower(Version),
num_id = parse_number(ID),
Question = recode(Question, 'unpleasantness' = 'displeasing', 'pain' = 'painful'),
Stimulus = case_match(Valence, 'EASE' ~ 'no_pain', 'PAIN' ~ 'pain')
) |>
mutate(Answer = if_else(Answer < 1, 1, Answer)) |>
mutate(Answer = if_else(num_id > 20, 1 + round((Answer-1)*96/144), Answer)) |>
mutate_if(is.character, as.factor) |>
relocate(Subject, .after = last_col()) |>
filter(!(num_id %in% xclude))
write.csv(answers_df, 'data/painEmpathy_2023_answers_clean_2_versions_pilot_biosemi.csv', row.names = FALSE)
erp_averages <- list.files(path = './data/ave_volt_2_versions', full.names = TRUE)
painEmpathy_data <- vroom::vroom(erp_averages, show_col_types = FALSE) |>
separate(ERPset, c('ID', 'Version'), sep = '_', remove = FALSE) |>
rename(Component = mlabel,
Amplitude = value,
Stimulus = binlabel,
Electrode = chlabel) |>
mutate(worklat = gsub('.0', '', worklat, fixed = TRUE),
num_id = parse_number(ID),
chindex = factor(chindex),
bini = factor(bini)) |>
left_join(unique(answers_df[c('num_id', 'Sex')]), by = 'num_id') |> mutate_if(is.character, as.factor) |>
mutate_if(is.character, as.factor) |>
mutate(Component = factor(Component, levels = c('N1', 'N2', 'P1', 'P3', 'LPP')),
Electrode = factor(Electrode, levels = c('FC1', 'Fz', 'FC2',
'CP1', 'Cz', 'CP2',
'O1' , 'Oz', 'O2')),
) |>
filter(!(num_id %in% xclude))
write.csv(painEmpathy_data, file.path('data/painEmpathy_2024_data_clean_2_versions_pilot_biosemi.csv'), row.names = FALSE)Answers
Code
addmargins(xtabs(~ Version + Sex, data = unique(answers_df[c('Version', 'Sex', 'num_id')])), 2) Sex
Version female male Sum
v1 19 11 30
v2 19 11 30Code
summary(answers_df) ID Version Sex Block trialN
pilot07: 121 v1:1425 female:1895 1:1453 Min. : 1.00
pilot04: 106 v2:1495 male :1025 2:1467 1st Qu.:16.00
pilot09: 106 Median :32.00
pilot30: 105 Mean :31.85
pilot14: 104 3rd Qu.:47.00
pilot22: 104 Max. :64.00
(Other):2274
Image Valence Answer Question rT
hp-5 : 19 EASE:1434 Min. : 1.00 displeasing:1479 Min. : 267
10.1.jpg: 18 PAIN:1486 1st Qu.: 1.00 painful :1441 1st Qu.: 3711
2.2.jpg : 18 Median :49.00 Median : 4543
fp-28 : 18 Mean :43.17 Mean : 4993
hp-27 : 18 3rd Qu.:77.00 3rd Qu.: 5691
fnp-19 : 17 Max. :97.00 Max. :94218
(Other) :2812
Session num_id Stimulus
Min. :1.000 Min. : 1.00 no_pain:1434
1st Qu.:1.000 1st Qu.: 8.00 pain :1486
Median :1.000 Median :15.00
Mean :1.054 Mean :15.55
3rd Qu.:1.000 3rd Qu.:24.00
Max. :3.000 Max. :31.00
Subject
pilot07_v1_2023-11-02_14-27-38: 62
pilot04_v2 : 60
pilot01_v2 : 59
pilot07_v2_2023-11-02_14-12-05: 59
pilot18_v2_2023-11-14_21-04-52: 57
pilot22_v2_2025-03-17_17-39-34: 56
(Other) :2567 Pain rating:
Code
painrat_data <- subset(answers_df, Question == 'painful')
pain_rating_lmer <- lmer(Answer ~ Stimulus*Version + (Stimulus*Version|num_id) + (1|Image), painrat_data)
afex_plot(
pain_rating_lmer,
x = 'Stimulus',
trace = 'Version',
id = 'num_id',
error_arg = list(width = .15),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
options(width = 120)
summary(pain_rating_lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Answer ~ Stimulus * Version + (Stimulus * Version | num_id) + (1 | Image)
Data: painrat_data
REML criterion at convergence: 12385.8
Scaled residuals:
Min 1Q Median 3Q Max
-4.6953 -0.4156 -0.0620 0.4121 4.3199
Random effects:
Groups Name Variance Std.Dev. Corr
Image (Intercept) 44.27 6.654
num_id (Intercept) 188.63 13.734
Stimuluspain 350.60 18.724 -0.74
Versionv2 63.05 7.940 -0.74 0.51
Stimuluspain:Versionv2 79.01 8.889 0.41 -0.65 -0.75
Residual 252.29 15.884
Number of obs: 1441, groups: Image, 312; num_id, 30
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 12.251 2.793 36.391 4.386 9.47e-05 ***
Stimuluspain 63.960 3.832 35.526 16.692 < 2e-16 ***
Versionv2 -2.027 2.223 50.154 -0.912 0.366
Stimuluspain:Versionv2 1.798 2.881 53.142 0.624 0.535
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Stmlsp Vrsnv2
Stimuluspan -0.735
Versionv2 -0.675 0.474
Stmlspn:Vr2 0.397 -0.596 -0.721Code
r2(pain_rating_lmer)# R2 for Mixed Models
Conditional R2: 0.829
Marginal R2: 0.714Code
icc(pain_rating_lmer)# Intraclass Correlation Coefficient
Adjusted ICC: 0.403
Unadjusted ICC: 0.115Code
ranova(pain_rating_lmer, reduce.terms = FALSE)ANOVA-like table for random-effects: Single term deletions
Model:
Answer ~ Stimulus + Version + (Stimulus * Version | num_id) + (1 | Image) + Stimulus:Version
npar logLik AIC LRT Df Pr(>Chisq)
<none> 16 -6192.9 12418
(Stimulus * Version | num_id) 6 -6393.2 12798 400.64 10 < 2.2e-16 ***
(1 | Image) 15 -6216.8 12464 47.73 1 4.901e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
anova(pain_rating_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Stimulus 104323 104323 1 33.109 413.4998 <2e-16 ***
Version 134 134 1 48.809 0.5298 0.4702
Stimulus:Version 98 98 1 53.142 0.3897 0.5351
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
interpret(omega_squared(pain_rating_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
-------------------------------------------------------------------
Stimulus | 0.92 | [0.87, 0.95] | large
Version | 0.00 | [0.00, 0.00] | very small
Stimulus:Version | 0.00 | [0.00, 0.00] | very small
- Interpretation rule: field2013Code
a_posteriori_lmer(pain_rating_lmer)____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Stimulus emmean SE df lower.CL upper.CL
no_pain 11.2 2.20 33.0 6.76 15.7
pain 76.1 2.05 33.7 71.93 80.3
Results are averaged over the levels of: Version
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
no_pain - pain -64.9 3.19 32.9 -20.324 <.0001
Results are averaged over the levels of: Version
Degrees-of-freedom method: kenward-roger Unpleasantness rating:
Code
unplsnt_data <- subset(answers_df, Question == 'displeasing')
unpleasantness_rating_lmer <- lmer(Answer ~ Stimulus*Version + (Stimulus*Version|num_id) + (1|Image), unplsnt_data)
afex_plot(
unpleasantness_rating_lmer,
x = 'Stimulus',
trace = 'Version',
id = 'num_id',
error_arg = list(width = .15),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 4)
)
Code
options(width = 120)
summary(unpleasantness_rating_lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Answer ~ Stimulus * Version + (Stimulus * Version | num_id) + (1 | Image)
Data: unplsnt_data
REML criterion at convergence: 12989.1
Scaled residuals:
Min 1Q Median 3Q Max
-4.2924 -0.4956 -0.0421 0.4650 3.2978
Random effects:
Groups Name Variance Std.Dev. Corr
Image (Intercept) 82.64 9.091
num_id (Intercept) 185.44 13.618
Stimuluspain 264.21 16.255 -0.67
Versionv2 47.85 6.918 -0.78 0.57
Stimuluspain:Versionv2 50.91 7.135 0.24 -0.47 -0.32
Residual 290.17 17.035
Number of obs: 1479, groups: Image, 315; num_id, 30
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 17.366 2.922 39.543 5.942 5.93e-07 ***
Stimuluspain 49.285 3.653 44.812 13.491 < 2e-16 ***
Versionv2 -4.929 2.421 68.830 -2.036 0.0456 *
Stimuluspain:Versionv2 7.297 3.171 87.050 2.301 0.0238 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Stmlsp Vrsnv2
Stimuluspan -0.686
Versionv2 -0.677 0.507
Stmlspn:Vr2 0.338 -0.549 -0.624Code
r2(unpleasantness_rating_lmer)# R2 for Mixed Models
Conditional R2: 0.762
Marginal R2: 0.580Code
icc(unpleasantness_rating_lmer)# Intraclass Correlation Coefficient
Adjusted ICC: 0.433
Unadjusted ICC: 0.182Code
ranova(unpleasantness_rating_lmer, reduce.terms = FALSE)ANOVA-like table for random-effects: Single term deletions
Model:
Answer ~ Stimulus + Version + (Stimulus * Version | num_id) + (1 | Image) + Stimulus:Version
npar logLik AIC LRT Df Pr(>Chisq)
<none> 16 -6494.5 13021
(Stimulus * Version | num_id) 6 -6671.3 13355 353.50 10 < 2.2e-16 ***
(1 | Image) 15 -6553.2 13136 117.33 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
anova(unpleasantness_rating_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Stimulus 85574 85574 1 38.003 294.9039 < 2e-16 ***
Version 133 133 1 57.471 0.4570 0.50172
Stimulus:Version 1537 1537 1 87.050 5.2963 0.02376 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
interpret(omega_squared(unpleasantness_rating_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
-------------------------------------------------------------------
Stimulus | 0.88 | [0.80, 0.92] | large
Version | 0.00 | [0.00, 0.00] | very small
Stimulus:Version | 0.05 | [0.00, 0.16] | small
- Interpretation rule: field2013Code
a_posteriori_lmer(unpleasantness_rating_lmer)____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Stimulus emmean SE df lower.CL upper.CL
no_pain 14.9 2.29 36.7 10.3 19.5
pain 67.8 2.30 36.5 63.2 72.5
Results are averaged over the levels of: Version
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
no_pain - pain -52.9 3.08 37.5 -17.160 <.0001
Results are averaged over the levels of: Version
Degrees-of-freedom method: kenward-roger
____________________________________________________________
$emmeans
Stimulus Version emmean SE df lower.CL upper.CL
no_pain v1 17.4 2.92 40.2 11.46 23.3
pain v1 66.7 2.69 42.3 61.22 72.1
no_pain v2 12.4 2.20 45.5 8.01 16.9
pain v2 69.0 2.55 40.4 63.86 74.2
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
no_pain v1 - pain v1 -49.28 3.66 44.1 -13.481 <.0001
no_pain v1 - no_pain v2 4.93 2.43 70.0 2.031 0.1867
no_pain v1 - pain v2 -51.65 4.00 39.6 -12.925 <.0001
pain v1 - no_pain v2 54.21 3.20 47.1 16.925 <.0001
pain v1 - pain v2 -2.37 2.52 64.4 -0.939 0.7841
no_pain v2 - pain v2 -56.58 3.27 43.5 -17.280 <.0001
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 4 estimates ERP plots
BDF files were pre-processed in Matlab using the EEGLAB 2023.1 toolbox (Delorme and Makeig 2004), the ERPLAB 10.0 toolbox (Lopez-Calderon and Luck 2014), and automated with in-house scripts. EEG data was high-pass filtered at 0.5 Hz with a 12 dB/oct roll-off. A working time window was selected from 2 seconds before the first Stimulus to 2 seconds after the last one. Defective channels were identified by eye inspection and omitted from the preprocessing steps. Artifacts originating from eye blinks or movements, heart beats, muscle contraction, channel noise, or electrical interference were identified and removed by means of independent Component analysis (ICA) and using the ICLabel 1.4 classifier (Pion-Tonachini, Kreutz-Delgado, and Makeig 2019), Components with an score between 0.8 and 1 in the aforementioned artifactual categories were removed. At this point defective channels were spherically interpolated. Data was re-referenced to infinity with the REST 1.2 toolbox (Dong et al. 2017), low-pass filtered at 35 Hz with a 12 dB/oct roll-off, cut in [-200ms 1000ms] epochs around stimuli, and baseline-corrected relative to the mean voltage in the pre-Stimulus time.
Topographic layout:
ERPs General description
Code
options(width = 90)
ggplot(
painEmpathy_data, aes(x = Amplitude, fill = Component, color = Component)) +
geom_histogram(alpha = .4) +
facet_wrap(~Component, ncol = 1) +
theme(strip.text.x = element_blank())
addmargins(xtabs(~ Version + Sex, data = unique(painEmpathy_data[c('Version', 'Sex', 'num_id')])), 2) Sex
Version female male Sum
v1 19 11 30
v2 19 11 30Code
summary(painEmpathy_data) worklat Component Amplitude chindex Electrode bini
[140 170]:360 N1 :360 Min. :-14.9196 11 :240 FC1 :240 1:900
[150 180]:360 N2 :360 1st Qu.: -2.4596 27 :240 Fz :240 2:900
[270 330]:360 P1 :360 Median : 1.0775 29 :240 FC2 :240
[280 380]:360 P3 :360 Mean : 0.6984 38 :240 O1 :240
[600 800]:360 LPP:360 3rd Qu.: 3.3000 46 :240 Oz :240
Max. : 15.9852 64 :240 O2 :240
(Other):360 (Other):360
Stimulus ERPset ID Version num_id Sex
no_pain:900 pilot01_v1: 30 pilot01: 60 v1:900 Min. : 1.00 female:1140
pain :900 pilot01_v2: 30 pilot02: 60 v2:900 1st Qu.: 8.00 male : 660
pilot02_v1: 30 pilot03: 60 Median :15.50
pilot02_v2: 30 pilot04: 60 Mean :15.77
pilot03_v1: 30 pilot05: 60 3rd Qu.:24.00
pilot03_v2: 30 pilot06: 60 Max. :31.00
(Other) :1620 (Other):1440 Code
n1_data <- subset(painEmpathy_data, Component == 'N1',)
n2_data <- subset(painEmpathy_data, Component == 'N2',)
p1_data <- subset(painEmpathy_data, Component == 'P1',)
p3_data <- subset(painEmpathy_data, Component == 'P3',)
lpp_data <- subset(painEmpathy_data, Component == 'LPP',)
In the front: N1 & N2:
N1, average from [150 180] ms interval
Electrodes FC1, Fz, FC2
Code
painEmpathy_n1_lmer <- lmer(Amplitude ~ Stimulus * Version + (Stimulus * Version | num_id) + (1 | num_id : Electrode), n1_data)
afex_plot(
painEmpathy_n1_lmer,
x = 'Stimulus',
trace = 'Version',
id = 'num_id',
error_arg = list(width = .25),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
Code
options(width = 120)
summary(painEmpathy_n1_lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Amplitude ~ Stimulus * Version + (Stimulus * Version | num_id) + (1 | num_id:Electrode)
Data: n1_data
REML criterion at convergence: 685.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.0512 -0.4869 0.0043 0.5047 2.0954
Random effects:
Groups Name Variance Std.Dev. Corr
num_id:Electrode (Intercept) 0.13664 0.3696
num_id (Intercept) 3.70535 1.9249
Stimuluspain 0.50392 0.7099 -0.45
Versionv2 1.24335 1.1151 -0.67 0.50
Stimuluspain:Versionv2 1.39140 1.1796 0.55 -0.80 -0.56
Residual 0.09341 0.3056
Number of obs: 360, groups: num_id:Electrode, 90; num_id, 30
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -2.86201 0.35506 28.99052 -8.061 6.89e-09 ***
Stimuluspain -0.05899 0.13738 28.99902 -0.429 0.671
Versionv2 0.18241 0.20862 28.99682 0.874 0.389
Stimuluspain:Versionv2 -0.15562 0.22479 28.99804 -0.692 0.494
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Stmlsp Vrsnv2
Stimuluspan -0.446
Versionv2 -0.657 0.501
Stmlspn:Vr2 0.536 -0.788 -0.570Code
r2(painEmpathy_n1_lmer)# R2 for Mixed Models
Conditional R2: 0.971
Marginal R2: 0.003Code
icc(painEmpathy_n1_lmer)# Intraclass Correlation Coefficient
Adjusted ICC: 0.971
Unadjusted ICC: 0.968Code
ranova(painEmpathy_n1_lmer, reduce.terms = FALSE)ANOVA-like table for random-effects: Single term deletions
Model:
Amplitude ~ Stimulus + Version + (Stimulus * Version | num_id) + (1 | num_id:Electrode) + Stimulus:Version
npar logLik AIC LRT Df Pr(>Chisq)
<none> 16 -342.89 717.77
(Stimulus * Version | num_id) 6 -539.65 1091.29 393.52 10 < 2.2e-16 ***
(1 | num_id:Electrode) 15 -393.31 816.62 100.84 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
anova(painEmpathy_n1_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Stimulus 0.244214 0.244214 1 28.999 2.6145 0.1167
Version 0.034714 0.034714 1 28.998 0.3716 0.5469
Stimulus:Version 0.044769 0.044769 1 28.998 0.4793 0.4943Code
interpret(omega_squared(painEmpathy_n1_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
-------------------------------------------------------------------
Stimulus | 0.05 | [0.00, 0.26] | small
Version | 0.00 | [0.00, 0.00] | very small
Stimulus:Version | 0.00 | [0.00, 0.00] | very small
- Interpretation rule: field2013Code
a_posteriori_lmer(painEmpathy_n1_lmer)N1 model check
Code
check_model(painEmpathy_n1_lmer)
N2, average from [270 330] ms interval
Electrodes FC1, Fz, FC2
Code
painEmpathy_n2_lmer <- lmer(Amplitude ~ Stimulus * Version + (Stimulus * Version | num_id) + (1 | num_id : Electrode), n2_data)
afex_plot(
painEmpathy_n2_lmer,
x = 'Stimulus',
trace = 'Version',
id = 'num_id',
error_arg = list(width = .25),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
Code
options(width = 120)
summary(painEmpathy_n2_lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Amplitude ~ Stimulus * Version + (Stimulus * Version | num_id) + (1 | num_id:Electrode)
Data: n2_data
REML criterion at convergence: 795.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.39271 -0.48281 0.02937 0.44161 2.27512
Random effects:
Groups Name Variance Std.Dev. Corr
num_id:Electrode (Intercept) 0.57658 0.7593
num_id (Intercept) 6.16661 2.4833
Stimuluspain 0.68295 0.8264 -0.36
Versionv2 0.98177 0.9908 -0.37 0.38
Stimuluspain:Versionv2 1.17877 1.0857 0.31 -0.76 -0.52
Residual 0.09321 0.3053
Number of obs: 360, groups: num_id:Electrode, 90; num_id, 30
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -4.42244 0.46151 28.99883 -9.582 1.72e-10 ***
Stimuluspain -0.05740 0.15760 28.99784 -0.364 0.718
Versionv2 1.41284 0.18654 28.99570 7.574 2.38e-08 ***
Stimuluspain:Versionv2 -0.08916 0.20841 28.99689 -0.428 0.672
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Stmlsp Vrsnv2
Stimuluspan -0.357
Versionv2 -0.365 0.390
Stmlspn:Vr2 0.299 -0.753 -0.534Code
r2(painEmpathy_n2_lmer)# R2 for Mixed Models
Conditional R2: 0.986
Marginal R2: 0.070Code
icc(painEmpathy_n2_lmer)# Intraclass Correlation Coefficient
Adjusted ICC: 0.985
Unadjusted ICC: 0.916Code
ranova(painEmpathy_n2_lmer, reduce.terms = FALSE)ANOVA-like table for random-effects: Single term deletions
Model:
Amplitude ~ Stimulus + Version + (Stimulus * Version | num_id) + (1 | num_id:Electrode) + Stimulus:Version
npar logLik AIC LRT Df Pr(>Chisq)
<none> 16 -397.57 827.14
(Stimulus * Version | num_id) 6 -566.16 1144.33 337.18 10 < 2.2e-16 ***
(1 | num_id:Electrode) 15 -536.78 1103.55 278.41 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
anova(painEmpathy_n2_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Stimulus 0.0884 0.0884 1 29.000 0.9486 0.3381
Version 7.0118 7.0118 1 28.997 75.2238 1.504e-09 ***
Stimulus:Version 0.0171 0.0171 1 28.997 0.1830 0.6720
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
interpret(omega_squared(painEmpathy_n2_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
-------------------------------------------------------------------
Stimulus | 0.00 | [0.00, 0.00] | very small
Version | 0.71 | [0.50, 0.81] | large
Stimulus:Version | 0.00 | [0.00, 0.00] | very small
- Interpretation rule: field2013Code
a_posteriori_lmer(painEmpathy_n2_lmer)____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Version emmean SE df lower.CL upper.CL
v1 -4.45 0.440 29 -5.35 -3.55
v2 -3.08 0.428 29 -3.96 -2.21
Results are averaged over the levels of: Stimulus
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
v1 - v2 -1.37 0.158 29 -8.673 <.0001
Results are averaged over the levels of: Stimulus
Degrees-of-freedom method: kenward-roger N2 model check
Code
check_model(painEmpathy_n2_lmer)
In the back: P1 & P3:
P1, average [140 170] ms interval
Electrodes O1, Oz, O2
Code
painEmpathy_p1_lmer <- lmer(Amplitude ~ Stimulus * Version + (Stimulus * Version | num_id) + (1 | num_id : Electrode), p1_data)
afex_plot(
painEmpathy_p1_lmer,
x = 'Stimulus',
trace = 'Version',
id = 'num_id',
error_arg = list(width = .25),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
Code
options(width = 120)
summary(painEmpathy_p1_lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Amplitude ~ Stimulus * Version + (Stimulus * Version | num_id) + (1 | num_id:Electrode)
Data: p1_data
REML criterion at convergence: 1064.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.5676 -0.3373 0.0015 0.3524 3.3907
Random effects:
Groups Name Variance Std.Dev. Corr
num_id:Electrode (Intercept) 1.3857 1.1772
num_id (Intercept) 13.7168 3.7036
Stimuluspain 0.7017 0.8377 0.08
Versionv2 1.5938 1.2625 -0.57 -0.04
Stimuluspain:Versionv2 1.1538 1.0742 0.05 -0.78 -0.20
Residual 0.2414 0.4914
Number of obs: 360, groups: num_id:Electrode, 90; num_id, 30
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 3.56105 0.68942 28.99822 5.165 1.6e-05 ***
Stimuluspain -0.10485 0.16958 29.00117 -0.618 0.541
Versionv2 -0.06164 0.24185 28.99996 -0.255 0.801
Stimuluspain:Versionv2 0.21698 0.22179 28.99990 0.978 0.336
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Stmlsp Vrsnv2
Stimuluspan 0.044
Versionv2 -0.552 0.030
Stmlspn:Vr2 0.062 -0.768 -0.271Code
r2(painEmpathy_p1_lmer)# R2 for Mixed Models
Conditional R2: 0.983
Marginal R2: 0.000Code
icc(painEmpathy_p1_lmer)# Intraclass Correlation Coefficient
Adjusted ICC: 0.983
Unadjusted ICC: 0.982Code
ranova(painEmpathy_p1_lmer, reduce.terms = FALSE)boundary (singular) fit: see help('isSingular')ANOVA-like table for random-effects: Single term deletions
Model:
Amplitude ~ Stimulus + Version + (Stimulus * Version | num_id) + (1 | num_id:Electrode) + Stimulus:Version
npar logLik AIC LRT Df Pr(>Chisq)
<none> 16 -532.03 1096.1
(Stimulus * Version | num_id) 6 -683.12 1378.2 302.17 10 < 2.2e-16 ***
(1 | num_id:Electrode) 15 -668.42 1366.8 272.77 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
anova(painEmpathy_p1_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Stimulus 0.000263 0.000263 1 29.002 0.0011 0.9739
Version 0.009426 0.009426 1 29.003 0.0390 0.8448
Stimulus:Version 0.231085 0.231085 1 29.000 0.9571 0.3360Code
interpret(omega_squared(painEmpathy_p1_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
-------------------------------------------------------------------
Stimulus | 0.00 | [0.00, 0.00] | very small
Version | 0.00 | [0.00, 0.00] | very small
Stimulus:Version | 0.00 | [0.00, 0.00] | very small
- Interpretation rule: field2013Code
a_posteriori_lmer(painEmpathy_p1_lmer)P1 model check
Code
check_model(painEmpathy_p1_lmer)
P3, average from [280 380] ms interval
Electrodes O1, Oz, O2
Code
painEmpathy_p3_lmer <- lmer(Amplitude ~ Stimulus * Version + (Stimulus * Version | num_id) + (1 | num_id : Electrode), p3_data)
afex_plot(
painEmpathy_p3_lmer,
x = 'Stimulus',
trace = 'Version',
id = 'num_id',
error_arg = list(width = .25),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
Code
options(width = 120)
summary(painEmpathy_p3_lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Amplitude ~ Stimulus * Version + (Stimulus * Version | num_id) + (1 | num_id:Electrode)
Data: p3_data
REML criterion at convergence: 854.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.93599 -0.36962 -0.02765 0.37979 2.27960
Random effects:
Groups Name Variance Std.Dev. Corr
num_id:Electrode (Intercept) 0.7103 0.8428
num_id (Intercept) 10.9062 3.3025
Stimuluspain 0.7202 0.8486 -0.28
Versionv2 0.6248 0.7904 -0.39 0.35
Stimuluspain:Versionv2 1.1681 1.0808 0.10 -0.67 -0.33
Residual 0.1094 0.3308
Number of obs: 360, groups: num_id:Electrode, 90; num_id, 30
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.68487 0.61045 28.99177 9.313 3.25e-10 ***
Stimuluspain -0.53754 0.16260 28.99728 -3.306 0.00253 **
Versionv2 -0.96415 0.15251 28.99671 -6.322 6.61e-07 ***
Stimuluspain:Versionv2 -0.09064 0.20929 28.99796 -0.433 0.66816
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Stmlsp Vrsnv2
Stimuluspan -0.273
Versionv2 -0.373 0.365
Stmlspn:Vr2 0.102 -0.675 -0.367Code
r2(painEmpathy_p3_lmer)# R2 for Mixed Models
Conditional R2: 0.990
Marginal R2: 0.031Code
icc(painEmpathy_p3_lmer)# Intraclass Correlation Coefficient
Adjusted ICC: 0.990
Unadjusted ICC: 0.959Code
ranova(painEmpathy_p3_lmer, reduce.terms = FALSE)ANOVA-like table for random-effects: Single term deletions
Model:
Amplitude ~ Stimulus + Version + (Stimulus * Version | num_id) + (1 | num_id:Electrode) + Stimulus:Version
npar logLik AIC LRT Df Pr(>Chisq)
<none> 16 -427.22 886.43
(Stimulus * Version | num_id) 6 -596.97 1205.94 339.50 10 < 2.2e-16 ***
(1 | num_id:Electrode) 15 -570.04 1170.09 285.65 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
anova(painEmpathy_p3_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Stimulus 2.5808 2.5808 1 29.000 23.5822 3.783e-05 ***
Version 4.9583 4.9583 1 28.998 45.3064 2.192e-07 ***
Stimulus:Version 0.0205 0.0205 1 28.998 0.1876 0.6682
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
interpret(omega_squared(painEmpathy_p3_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
-------------------------------------------------------------------
Stimulus | 0.42 | [0.15, 0.62] | large
Version | 0.59 | [0.34, 0.73] | large
Stimulus:Version | 0.00 | [0.00, 0.00] | very small
- Interpretation rule: field2013Code
a_posteriori_lmer(painEmpathy_p3_lmer)____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Stimulus emmean SE df lower.CL upper.CL
no_pain 5.20 0.586 29 4.00 6.40
pain 4.62 0.566 29 3.46 5.78
Results are averaged over the levels of: Version
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
no_pain - pain 0.583 0.12 29 4.856 <.0001
Results are averaged over the levels of: Version
Degrees-of-freedom method: kenward-roger
____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Version emmean SE df lower.CL upper.CL
v1 5.42 0.593 29 4.20 6.63
v2 4.41 0.562 29 3.26 5.56
Results are averaged over the levels of: Stimulus
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
v1 - v2 1.01 0.15 29 6.731 <.0001
Results are averaged over the levels of: Stimulus
Degrees-of-freedom method: kenward-roger P3 model check
Code
check_model(painEmpathy_p3_lmer)
Late Centro-parietal: LPP
LPP, average from [600 800] ms interval
Electrodes CP1, Cz, CP2
Code
painEmpathy_lpp_lmer <- lmer(Amplitude ~ Stimulus * Version + (Stimulus * Version | num_id) + (1 | num_id : Electrode), lpp_data)
afex_plot(
painEmpathy_lpp_lmer,
x = 'Stimulus',
trace = 'Version',
id = 'num_id',
error_arg = list(width = .25),
dodge = my_dodge,
data_arg = list(
position =
position_jitterdodge(
jitter.width = my_jitter,
jitter.height = 0,
dodge.width = my_dodge ## needs to be same as dodge
)),
mapping = c('color'),
point_arg = list(size = 3)
)
Code
options(width = 120)
summary(painEmpathy_lpp_lmer)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Amplitude ~ Stimulus * Version + (Stimulus * Version | num_id) + (1 | num_id:Electrode)
Data: lpp_data
REML criterion at convergence: 545.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.39188 -0.50031 0.01664 0.46119 2.06278
Random effects:
Groups Name Variance Std.Dev. Corr
num_id:Electrode (Intercept) 0.19443 0.4409
num_id (Intercept) 0.72566 0.8519
Stimuluspain 0.30631 0.5535 -0.20
Versionv2 0.34942 0.5911 -0.49 0.32
Stimuluspain:Versionv2 0.43915 0.6627 0.43 -0.50 -0.39
Residual 0.06501 0.2550
Number of obs: 360, groups: num_id:Electrode, 90; num_id, 30
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.34474 0.16453 29.00090 8.173 5.18e-09 ***
Stimuluspain 0.59257 0.10796 28.99945 5.489 6.53e-06 ***
Versionv2 -0.08897 0.11442 29.00154 -0.778 0.4431
Stimuluspain:Versionv2 0.23278 0.13239 29.00272 1.758 0.0893 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Stmlsp Vrsnv2
Stimuluspan -0.214
Versionv2 -0.479 0.344
Stmlspn:Vr2 0.404 -0.527 -0.427Code
r2(painEmpathy_lpp_lmer)# R2 for Mixed Models
Conditional R2: 0.947
Marginal R2: 0.106Code
icc(painEmpathy_lpp_lmer)# Intraclass Correlation Coefficient
Adjusted ICC: 0.940
Unadjusted ICC: 0.840Code
ranova(painEmpathy_lpp_lmer, reduce.terms = FALSE)ANOVA-like table for random-effects: Single term deletions
Model:
Amplitude ~ Stimulus + Version + (Stimulus * Version | num_id) + (1 | num_id:Electrode) + Stimulus:Version
npar logLik AIC LRT Df Pr(>Chisq)
<none> 16 -272.83 577.65
(Stimulus * Version | num_id) 6 -397.97 807.93 250.28 10 < 2.2e-16 ***
(1 | num_id:Electrode) 15 -362.05 754.09 178.44 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
anova(painEmpathy_lpp_lmer)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Stimulus 3.8450 3.8450 1 28.997 59.1477 1.764e-08 ***
Version 0.0044 0.0044 1 29.000 0.0684 0.79560
Stimulus:Version 0.2010 0.2010 1 29.003 3.0914 0.08926 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Code
interpret(omega_squared(painEmpathy_lpp_lmer, alternative = 'two.sided'), rules = 'field2013')# Effect Size for ANOVA (Type III)
Parameter | Omega2 (partial) | 95% CI | Interpretation
-------------------------------------------------------------------
Stimulus | 0.65 | [0.42, 0.78] | large
Version | 0.00 | [0.00, 0.00] | very small
Stimulus:Version | 0.06 | [0.00, 0.28] | medium
- Interpretation rule: field2013Code
a_posteriori_lmer(painEmpathy_lpp_lmer)____________________________________________________________NOTE: Results may be misleading due to involvement in interactions$emmeans
Stimulus emmean SE df lower.CL upper.CL
no_pain 1.30 0.146 29 1.00 1.60
pain 2.01 0.179 29 1.64 2.38
Results are averaged over the levels of: Version
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
no_pain - pain -0.709 0.0922 29 -7.691 <.0001
Results are averaged over the levels of: Version
Degrees-of-freedom method: kenward-roger LPP model check
Code
check_model(painEmpathy_lpp_lmer)
Factorial Mass Univariate analysis (FMUT, cluster mass)
Fields and Kuperberg (2020)
max_dist value of 50 corresponds to an approximate distance of 5.24 cm (assuming a 56 cm great circle circumference head and that your electrode coordinates are based on an idealized spherical head with radius of 85.000000). Min/Max distances between all pairs of channels (in chanlocs units): 26.560450/169.891840 Median (semi-IQR) distance between all pairs of channels (in chanlocs units): 119.172654 (30.583399) Mean (SD) # of neighbors per channel: 2.6 (1.2) Median (semi-IQR) # of neighbors per channel: 2.0 (1.0) Min/max # of neighbors per channel: 0 to 4
Effects over time
painEmpathy_fmut_2_versions_pilot_biosemi: 1 significant StimulusXVersion cluster(s) out of 27 cluster 1 F-masss: 813 cluster 1 p-value: 0.0132 3 significant Stimulus cluster(s) out of 26 cluster 1 F-masss: 2445 cluster 1 p-value: 0.0065 cluster 2 F-masss: 13142 cluster 2 p-value: 0.0001 cluster 3 F-masss: 1228 cluster 3 p-value: 0.0261 3 significant Version cluster(s) out of 22 cluster 1 F-masss: 12389 cluster 1 p-value: 0.0001 cluster 2 F-masss: 3356 cluster 2 p-value: 0.0007 cluster 3 F-masss: 2330 cluster 3 p-value: 0.0021
Mass Univariate analysis (cluster-mass), full time window
max_dist value of 50 corresponds to an approximate distance of 5.24 cm (assuming a 56 cm great circle circumference head and that your electrode coordinates are based on an idealized spherical head with radius of 85.000000). Min/Max distances between all pairs of channels (in chanlocs units): 25.654041/169.962716 Median (semi-IQR) distance between all pairs of channels (in chanlocs units): 113.111431 (33.320704) Mean (SD) # of neighbors per channel: 6.5 (1.5) Median (semi-IQR) # of neighbors per channel: 7.0 (1.5) Min/max # of neighbors per channel: 3 to 8
Groppe, Urbach, and Kutas (2011)
painEmpathy_v1_mass_pilot_biosemi: 1 significant positive cluster(s) out of 3 cluster 1 t-masss: 1883 cluster 1 p-value: 0.0000 1 significant negative cluster(s) out of 24 cluster 1 t-masss: -514 cluster 1 p-value: 0.0092 painEmpathy_v2_mass_pilot_biosemi: 1 significant positive cluster(s) out of 10 cluster 1 t-masss: 2225 cluster 1 p-value: 0.0000 1 significant negative cluster(s) out of 20 cluster 1 t-masss: -358 cluster 1 p-value: 0.0236
Multivariate pattern analysis (decoding)
No processing besides a 0.1 Hz high-pass filter (12 dB/oct roll-off) and reference to infinity (Dong et al. 2017).
Using 13 central electrodes shared between BioSemi ActiveTwo and Emotiv Epoc Flex devices:
- F3, Fz, F4
- FC1, FC2
- C3, Cz, C4
- CP1 ,CP2
- P3, Pz, P4
Done in the ADAM 1.14 toolbox (Fahrenfort 2020) with FieldTrip 20211209 (Oostenveld et al. 2011).
Exemplar ERPs on Pz
Decoding over time
Decoding difference
Temporal generalization plot
Activation pattern
Temporal generalization over time
Individual results
References
Delorme, Arnaud, and Scott Makeig. 2004. “EEGLAB: An Open Source Toolbox for Analysis of Single-Trial EEG Dynamics Including Independent Component Analysis.” Journal of Neuroscience Methods 134 (March): 9–21. https://doi.org/10.1016/J.JNEUMETH.2003.10.009.
Dong, Li, Fali Li, Qiang Liu, Xin Wen, Yongxiu Lai, Peng Xu, and Dezhong Yao. 2017. “MATLAB Toolboxes for Reference Electrode Standardization Technique (REST) of Scalp EEG.” Frontiers in Neuroscience 11 (October): 601. https://doi.org/10.3389/fnins.2017.00601.
Fahrenfort, Johannes Jacobus. 2020. “Multivariate Methods to Track the Spatiotemporal Profile of Feature-Based Attentional Selection Using EEG.” Neuromethods 151: 129–56. https://doi.org/10.1007/7657_2019_26/COVER.
Fields, Eric C., and Gina R. Kuperberg. 2020. “Having Your Cake and Eating It Too: Flexibility and Power with Mass Univariate Statistics for ERP Data.” Psychophysiology 57 (February): e13468. https://doi.org/10.1111/PSYP.13468.
Groppe, David M., Thomas P. Urbach, and Marta Kutas. 2011. “Mass Univariate Analysis of Event-Related Brain Potentials/Fields i: A Critical Tutorial Review.” Psychophysiology 48 (December): 1711–25. https://doi.org/10.1111/J.1469-8986.2011.01273.X.
Lopez-Calderon, Javier, and Steven J. Luck. 2014. “ERPLAB: An Open-Source Toolbox for the Analysis of Event-Related Potentials.” Frontiers in Human Neuroscience 8 (April): 75729. https://doi.org/10.3389/FNHUM.2014.00213/BIBTEX.
Oostenveld, Robert, Pascal Fries, Eric Maris, and Jan Mathijs Schoffelen. 2011. “FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data.” Computational Intelligence and Neuroscience 2011. https://doi.org/10.1155/2011/156869.
Pion-Tonachini, Luca, Ken Kreutz-Delgado, and Scott Makeig. 2019. “ICLabel: An Automated Electroencephalographic Independent Component Classifier, Dataset, and Website.” NeuroImage 198: 181–97. https://doi.org/10.1016/j.neuroimage.2019.05.026.