VPI FOOOF Results
library(tidyr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionrequire(ggplot2)## Loading required package: ggplot2require(lme4)## Loading required package: lme4## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpackrequire(lmerTest)## Loading required package: lmerTest##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## stepsetwd("/Volumes/somrehab-ts/Groups/BorichLab/NB_VPI_EEG")
df = read.csv("fooof_results_all_subjects.csv", header = T)split condition into Limb, Feedback, and Posture
df_split <- df %>%
separate(condition, into = c("Limb", "Feedback", "Posture"), sep = "_") %>%
mutate(group = substr(subject, 1, 3))
df_split$onset <- factor(df_split$onset, levels = c("preonset", "postonset"))Split the data by limb
df_split_L = df_split[df_split$Limb == "LEFT" & df_split$channel == "CP4",]
df_split_R = df_split[df_split$Limb == "RIGHT" & df_split$channel == "CP3",]Plot Mu Right: Power
ggplot(data = df_split_R, aes(x = onset, y = alpha_power)) +
stat_summary(fun = "mean", geom = "col", position = "dodge") +
geom_point(position = position_dodge(0.9)) +
geom_line(aes(group = subject))+
facet_grid(rows = vars(group), cols = vars(Posture, Feedback))+
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylab("Mu Power")## Warning: Removed 34 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 34 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 17 rows containing missing values or values outside the scale range
## (`geom_line()`).
#ggsave(plot,"alpha.jpg", width = 6, height = 4, unit = 'in', dpi = 300)Plot Mu Left: Power
ggplot(data = df_split_L, aes(x = onset, y = alpha_power)) +
stat_summary(fun = "mean", geom = "col", position = "dodge") +
geom_point(position = position_dodge(0.9)) +
geom_line(aes(group = subject))+
facet_grid(rows = vars(group), cols = vars(Posture, Feedback))+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
ylab("Mu Power")## Warning: Removed 33 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 33 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_line()`).
LMER Mu Power: Right Limb
model = lmer(alpha_power~onset*Feedback*Posture+(1|subject), data = df_split_R)
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: alpha_power ~ onset * Feedback * Posture + (1 | subject)
## Data: df_split_R
##
## REML criterion at convergence: -129.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2416 -0.5845 0.0100 0.4808 3.1140
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.07620 0.2760
## Residual 0.01847 0.1359
## Number of obs: 238, groups: subject, 33
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.773127 0.053762 46.451409
## onsetpostonset -0.206478 0.034992 198.147917
## FeedbackVIS -0.072471 0.034414 198.199709
## PostureSTAND -0.100980 0.034753 198.245758
## onsetpostonset:FeedbackVIS -0.033408 0.050000 198.220706
## onsetpostonset:PostureSTAND 0.006462 0.050075 198.319970
## FeedbackVIS:PostureSTAND 0.120534 0.048769 198.263442
## onsetpostonset:FeedbackVIS:PostureSTAND -0.011215 0.071095 198.208484
## t value Pr(>|t|)
## (Intercept) 14.381 < 2e-16 ***
## onsetpostonset -5.901 1.54e-08 ***
## FeedbackVIS -2.106 0.03648 *
## PostureSTAND -2.906 0.00408 **
## onsetpostonset:FeedbackVIS -0.668 0.50480
## onsetpostonset:PostureSTAND 0.129 0.89744
## FeedbackVIS:PostureSTAND 2.472 0.01430 *
## onsetpostonset:FeedbackVIS:PostureSTAND -0.158 0.87482
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) onstps FdbVIS PSTAND on:FVIS o:PSTA FVIS:P
## onsetpstnst -0.307
## FeedbackVIS -0.314 0.483
## PosturSTAND -0.311 0.478 0.490
## onstps:FVIS 0.216 -0.700 -0.688 -0.337
## onst:PSTAND 0.214 -0.702 -0.340 -0.692 0.495
## FVIS:PSTAND 0.222 -0.343 -0.708 -0.715 0.488 0.497
## o:FVIS:PSTA -0.151 0.495 0.484 0.487 -0.704 -0.705 -0.683LMER Mu Power: Left Limb
model = lmer(alpha_power~onset*Feedback*Posture+(1|subject), data = df_split_L)
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: alpha_power ~ onset * Feedback * Posture + (1 | subject)
## Data: df_split_L
##
## REML criterion at convergence: -133.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5251 -0.6212 -0.0276 0.5033 3.8793
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.07703 0.2775
## Residual 0.01798 0.1341
## Number of obs: 239, groups: subject, 34
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.723848 0.053043 46.948708
## onsetpostonset -0.181192 0.034491 198.329955
## FeedbackVIS 0.008737 0.034390 198.224016
## PostureSTAND -0.048440 0.033417 198.038147
## onsetpostonset:FeedbackVIS -0.104543 0.049811 198.401451
## onsetpostonset:PostureSTAND 0.023753 0.049300 198.177416
## FeedbackVIS:PostureSTAND 0.010468 0.048398 198.181234
## onsetpostonset:FeedbackVIS:PostureSTAND 0.070274 0.070107 198.207961
## t value Pr(>|t|)
## (Intercept) 13.646 < 2e-16 ***
## onsetpostonset -5.253 3.84e-07 ***
## FeedbackVIS 0.254 0.7997
## PostureSTAND -1.450 0.1488
## onsetpostonset:FeedbackVIS -2.099 0.0371 *
## onsetpostonset:PostureSTAND 0.482 0.6305
## FeedbackVIS:PostureSTAND 0.216 0.8290
## onsetpostonset:FeedbackVIS:PostureSTAND 1.002 0.3174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) onstps FdbVIS PSTAND on:FVIS o:PSTA FVIS:P
## onsetpstnst -0.300
## FeedbackVIS -0.299 0.466
## PosturSTAND -0.309 0.478 0.476
## onstps:FVIS 0.208 -0.694 -0.691 -0.331
## onst:PSTAND 0.210 -0.695 -0.321 -0.678 0.479
## FVIS:PSTAND 0.212 -0.327 -0.710 -0.690 0.487 0.468
## o:FVIS:PSTA -0.147 0.491 0.491 0.477 -0.708 -0.703 -0.689Plot Mu Right: AUC
ggplot(data = df_split_R, aes(x = onset, y = alpha_auc)) +
stat_summary(fun = "mean", geom = "col", position = "dodge") +
geom_point(position = position_dodge(0.9)) +
geom_line(aes(group = subject))+
facet_grid(rows = vars(group), cols = vars(Posture, Feedback))+
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylab("Mu AUC") +
ylim(0,10)## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_line()`).
#ggsave(plot,"alpha.jpg", width = 6, height = 4, unit = 'in', dpi = 300)Plot Mu Left: AUC
ggplot(data = df_split_L, aes(x = onset, y = alpha_auc)) +
stat_summary(fun = "mean", geom = "col", position = "dodge") +
geom_point(position = position_dodge(0.9)) +
geom_line(aes(group = subject))+
facet_grid(rows = vars(group), cols = vars(Posture, Feedback))+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
ylab("Mu AUC") +
ylim(0,10)## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_line()`).
LMER Mu AUC: Right Limb
model = lmer(alpha_auc~onset*Feedback*Posture+(1|subject), data = df_split_R)
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: alpha_auc ~ onset * Feedback * Posture + (1 | subject)
## Data: df_split_R
##
## REML criterion at convergence: 1059.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5202 -0.3599 -0.0110 0.3323 5.5334
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 6.442 2.538
## Residual 1.923 1.387
## Number of obs: 272, groups: subject, 34
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.8237 0.4960 51.2478 5.693
## onsetpostonset -1.1903 0.3363 231.0000 -3.539
## FeedbackVIS -0.5047 0.3363 231.0000 -1.501
## PostureSTAND -0.8133 0.3363 231.0000 -2.418
## onsetpostonset:FeedbackVIS 0.1290 0.4756 231.0000 0.271
## onsetpostonset:PostureSTAND 0.3394 0.4756 231.0000 0.714
## FeedbackVIS:PostureSTAND 0.9286 0.4756 231.0000 1.952
## onsetpostonset:FeedbackVIS:PostureSTAND -0.6118 0.6727 231.0000 -0.910
## Pr(>|t|)
## (Intercept) 6.1e-07 ***
## onsetpostonset 0.000485 ***
## FeedbackVIS 0.134803
## PostureSTAND 0.016377 *
## onsetpostonset:FeedbackVIS 0.786499
## onsetpostonset:PostureSTAND 0.476165
## FeedbackVIS:PostureSTAND 0.052107 .
## onsetpostonset:FeedbackVIS:PostureSTAND 0.364005
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) onstps FdbVIS PSTAND on:FVIS o:PSTA FVIS:P
## onsetpstnst -0.339
## FeedbackVIS -0.339 0.500
## PosturSTAND -0.339 0.500 0.500
## onstps:FVIS 0.240 -0.707 -0.707 -0.354
## onst:PSTAND 0.240 -0.707 -0.354 -0.707 0.500
## FVIS:PSTAND 0.240 -0.354 -0.707 -0.707 0.500 0.500
## o:FVIS:PSTA -0.170 0.500 0.500 0.500 -0.707 -0.707 -0.707LMER Mu AUC: Left Limb
model = lmer(alpha_auc~onset*Feedback*Posture+(1|subject), data = df_split_L)
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: alpha_auc ~ onset * Feedback * Posture + (1 | subject)
## Data: df_split_L
##
## REML criterion at convergence: 1249.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5497 -0.2888 -0.0220 0.2550 8.5674
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 8.404 2.899
## Residual 4.203 2.050
## Number of obs: 272, groups: subject, 34
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.73290 0.60892 64.22211 4.488
## onsetpostonset -1.19722 0.49721 231.00000 -2.408
## FeedbackVIS 0.30782 0.49721 231.00000 0.619
## PostureSTAND -0.30590 0.49721 231.00000 -0.615
## onsetpostonset:FeedbackVIS -0.73054 0.70316 231.00000 -1.039
## onsetpostonset:PostureSTAND 0.01692 0.70316 231.00000 0.024
## FeedbackVIS:PostureSTAND -0.15176 0.70316 231.00000 -0.216
## onsetpostonset:FeedbackVIS:PostureSTAND 0.42246 0.99442 231.00000 0.425
## Pr(>|t|)
## (Intercept) 3.05e-05 ***
## onsetpostonset 0.0168 *
## FeedbackVIS 0.5365
## PostureSTAND 0.5390
## onsetpostonset:FeedbackVIS 0.2999
## onsetpostonset:PostureSTAND 0.9808
## FeedbackVIS:PostureSTAND 0.8293
## onsetpostonset:FeedbackVIS:PostureSTAND 0.6714
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) onstps FdbVIS PSTAND on:FVIS o:PSTA FVIS:P
## onsetpstnst -0.408
## FeedbackVIS -0.408 0.500
## PosturSTAND -0.408 0.500 0.500
## onstps:FVIS 0.289 -0.707 -0.707 -0.354
## onst:PSTAND 0.289 -0.707 -0.354 -0.707 0.500
## FVIS:PSTAND 0.289 -0.354 -0.707 -0.707 0.500 0.500
## o:FVIS:PSTA -0.204 0.500 0.500 0.500 -0.707 -0.707 -0.707Plot Beta Right: Power
ggplot(data = df_split_R, aes(x = onset, y = beta_power)) +
stat_summary(fun = "mean", geom = "col", position = "dodge") +
geom_point(position = position_dodge(0.9)) +
geom_line(aes(group = subject))+
facet_grid(rows = vars(group), cols = vars(Posture, Feedback, Limb))+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
ylab("Beta Power")## Warning: Removed 26 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 26 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_line()`).
Plot Beta Left: Power
ggplot(data = df_split_L, aes(x = onset, y = beta_power)) +
stat_summary(fun = "mean", geom = "col", position = "dodge") +
geom_point(position = position_dodge(0.9)) +
geom_line(aes(group = subject))+
facet_grid(rows = vars(group), cols = vars(Posture, Feedback, Limb))+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
ylab("Beta Power")## Warning: Removed 18 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 18 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_line()`).
LMER Beta Power: Right Limb
model = lmer(beta_power~onset*Feedback*Posture+(1|subject), data = df_split_R)
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: beta_power ~ onset * Feedback * Posture + (1 | subject)
## Data: df_split_R
##
## REML criterion at convergence: -309
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6505 -0.5371 -0.0873 0.4329 4.2924
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.037522 0.19371
## Residual 0.008856 0.09411
## Number of obs: 246, groups: subject, 34
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 0.563914 0.037060 47.267528
## onsetpostonset -0.199292 0.024122 205.198189
## FeedbackVIS 0.004336 0.023030 204.925196
## PostureSTAND -0.073457 0.023463 205.104939
## onsetpostonset:FeedbackVIS -0.035971 0.034439 205.033535
## onsetpostonset:PostureSTAND 0.037004 0.034320 205.055766
## FeedbackVIS:PostureSTAND 0.022420 0.032733 204.990880
## onsetpostonset:FeedbackVIS:PostureSTAND 0.034530 0.048428 205.038655
## t value Pr(>|t|)
## (Intercept) 15.216 < 2e-16 ***
## onsetpostonset -8.262 1.76e-14 ***
## FeedbackVIS 0.188 0.851
## PostureSTAND -3.131 0.002 **
## onsetpostonset:FeedbackVIS -1.044 0.298
## onsetpostonset:PostureSTAND 1.078 0.282
## FeedbackVIS:PostureSTAND 0.685 0.494
## onsetpostonset:FeedbackVIS:PostureSTAND 0.713 0.477
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) onstps FdbVIS PSTAND on:FVIS o:PSTA FVIS:P
## onsetpstnst -0.300
## FeedbackVIS -0.316 0.483
## PosturSTAND -0.311 0.478 0.500
## onstps:FVIS 0.211 -0.694 -0.669 -0.334
## onst:PSTAND 0.211 -0.698 -0.339 -0.681 0.488
## FVIS:PSTAND 0.223 -0.343 -0.704 -0.717 0.471 0.488
## o:FVIS:PSTA -0.150 0.495 0.476 0.485 -0.712 -0.708 -0.676LMER Beta Power: Left Limb
model = lmer(beta_power~onset*Feedback*Posture+(1|subject), data = df_split_L)
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: beta_power ~ onset * Feedback * Posture + (1 | subject)
## Data: df_split_L
##
## REML criterion at convergence: -354.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.49461 -0.66460 -0.04673 0.63568 3.06037
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.035222 0.18768
## Residual 0.007681 0.08764
## Number of obs: 254, groups: subject, 34
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.54710 0.03552 45.90286 15.401
## onsetpostonset -0.18217 0.02280 213.28357 -7.989
## FeedbackVIS -0.00824 0.02145 213.06859 -0.384
## PostureSTAND -0.04662 0.02126 212.98847 -2.193
## onsetpostonset:FeedbackVIS -0.04358 0.03192 213.15638 -1.365
## onsetpostonset:PostureSTAND 0.01077 0.03156 213.17642 0.341
## FeedbackVIS:PostureSTAND 0.02920 0.03020 213.02891 0.967
## onsetpostonset:FeedbackVIS:PostureSTAND 0.03959 0.04438 213.18162 0.892
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## onsetpostonset 8.4e-14 ***
## FeedbackVIS 0.7013
## PostureSTAND 0.0294 *
## onsetpostonset:FeedbackVIS 0.1736
## onsetpostonset:PostureSTAND 0.7331
## FeedbackVIS:PostureSTAND 0.3348
## onsetpostonset:FeedbackVIS:PostureSTAND 0.3733
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) onstps FdbVIS PSTAND on:FVIS o:PSTA FVIS:P
## onsetpstnst -0.279
## FeedbackVIS -0.296 0.465
## PosturSTAND -0.299 0.466 0.495
## onstps:FVIS 0.199 -0.712 -0.672 -0.333
## onst:PSTAND 0.202 -0.722 -0.336 -0.674 0.514
## FVIS:PSTAND 0.211 -0.330 -0.710 -0.704 0.478 0.476
## o:FVIS:PSTA -0.143 0.514 0.485 0.479 -0.722 -0.712 -0.682Plot Beta Right: AUC
ggplot(data = df_split_R, aes(x = onset, y = beta_auc)) +
stat_summary(fun = "mean", geom = "col", position = "dodge") +
geom_point(position = position_dodge(0.9)) +
geom_line(aes(group = subject))+
facet_grid(rows = vars(group), cols = vars(Posture, Feedback, Limb))+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
ylab("Beta AUC")
Plot Beta Left: AUC
ggplot(data = df_split_L, aes(x = onset, y = beta_auc)) +
stat_summary(fun = "mean", geom = "col", position = "dodge") +
geom_point(position = position_dodge(0.9)) +
geom_line(aes(group = subject))+
facet_grid(rows = vars(group), cols = vars(Posture, Feedback, Limb))+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
ylab("Beta AUC")
LMER Beta AUC: Right Limb
model = lmer(beta_auc~onset*Feedback*Posture+(1|subject), data = df_split_R)
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: beta_auc ~ onset * Feedback * Posture + (1 | subject)
## Data: df_split_R
##
## REML criterion at convergence: 577.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2150 -0.5265 0.0761 0.4302 6.2334
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 1.6832 1.2974
## Residual 0.2889 0.5375
## Number of obs: 272, groups: subject, 34
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.86336 0.24084 43.28350 7.737
## onsetpostonset -0.56890 0.13036 231.00000 -4.364
## FeedbackVIS 0.20399 0.13036 231.00000 1.565
## PostureSTAND -0.12857 0.13036 231.00000 -0.986
## onsetpostonset:FeedbackVIS -0.28300 0.18436 231.00000 -1.535
## onsetpostonset:PostureSTAND -0.01075 0.18436 231.00000 -0.058
## FeedbackVIS:PostureSTAND 0.01527 0.18436 231.00000 0.083
## onsetpostonset:FeedbackVIS:PostureSTAND 0.08402 0.26072 231.00000 0.322
## Pr(>|t|)
## (Intercept) 1.07e-09 ***
## onsetpostonset 1.93e-05 ***
## FeedbackVIS 0.119
## PostureSTAND 0.325
## onsetpostonset:FeedbackVIS 0.126
## onsetpostonset:PostureSTAND 0.954
## FeedbackVIS:PostureSTAND 0.934
## onsetpostonset:FeedbackVIS:PostureSTAND 0.748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) onstps FdbVIS PSTAND on:FVIS o:PSTA FVIS:P
## onsetpstnst -0.271
## FeedbackVIS -0.271 0.500
## PosturSTAND -0.271 0.500 0.500
## onstps:FVIS 0.191 -0.707 -0.707 -0.354
## onst:PSTAND 0.191 -0.707 -0.354 -0.707 0.500
## FVIS:PSTAND 0.191 -0.354 -0.707 -0.707 0.500 0.500
## o:FVIS:PSTA -0.135 0.500 0.500 0.500 -0.707 -0.707 -0.707LMER Beta AUC: Left Limb
model = lmer(beta_auc~onset*Feedback*Posture+(1|subject), data = df_split_L)
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: beta_auc ~ onset * Feedback * Posture + (1 | subject)
## Data: df_split_L
##
## REML criterion at convergence: 617.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5856 -0.5091 0.0093 0.4895 6.7766
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 1.6078 1.2680
## Residual 0.3467 0.5888
## Number of obs: 272, groups: subject, 34
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.93927 0.23976 46.01948 8.088
## onsetpostonset -0.66591 0.14281 231.00000 -4.663
## FeedbackVIS 0.12095 0.14281 231.00000 0.847
## PostureSTAND -0.11936 0.14281 231.00000 -0.836
## onsetpostonset:FeedbackVIS -0.29997 0.20197 231.00000 -1.485
## onsetpostonset:PostureSTAND -0.02608 0.20197 231.00000 -0.129
## FeedbackVIS:PostureSTAND 0.07582 0.20197 231.00000 0.375
## onsetpostonset:FeedbackVIS:PostureSTAND 0.07338 0.28563 231.00000 0.257
## Pr(>|t|)
## (Intercept) 2.19e-10 ***
## onsetpostonset 5.28e-06 ***
## FeedbackVIS 0.398
## PostureSTAND 0.404
## onsetpostonset:FeedbackVIS 0.139
## onsetpostonset:PostureSTAND 0.897
## FeedbackVIS:PostureSTAND 0.708
## onsetpostonset:FeedbackVIS:PostureSTAND 0.797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) onstps FdbVIS PSTAND on:FVIS o:PSTA FVIS:P
## onsetpstnst -0.298
## FeedbackVIS -0.298 0.500
## PosturSTAND -0.298 0.500 0.500
## onstps:FVIS 0.211 -0.707 -0.707 -0.354
## onst:PSTAND 0.211 -0.707 -0.354 -0.707 0.500
## FVIS:PSTAND 0.211 -0.354 -0.707 -0.707 0.500 0.500
## o:FVIS:PSTA -0.149 0.500 0.500 0.500 -0.707 -0.707 -0.707To Do: -C3/C4, P3/P4, O1/O2? -adjust fooof parameters X-Include AUC in python script -don’t include 0 in pwelch -LIMO or extract and run in R