In livioivil/eegusta: EEG Utilities and STatistical Analysis: eeguana-based tools for statistical analysis of EEG data

knitr::opts_chunk$set(echo = TRUE)

eegUtils: data import and format (eeg_data and eeg_epochs)

library(eegUtils)
library(tidyverse)
library(data.table)

##########
# https://github.com/craddm/eegUtils
# eegUtils/vignettes/eegUtils.html
# eegUtils/vignettes/epoch-handling.html
# vedi anche:
# eegUtils/vignettes/time-frequency-analysis.Rmd

##########
# dal sito:
# https://osf.io/dxhjb/
# scaricare il file Matt-task-spatcue.bdf digitando:
# https://osf.io/hy5wq/download

# library(httr)
# GET("https://osf.io/hy5wq//?action=download",
#     write_disk("./Matt-task-spatcue.bdf", overwrite = TRUE),
#     progress()
# )

eeg_example <- import_raw("Matt-task-spatcue.bdf")

#str(eeg_example)

epoched_example <-
  epoch_data(
    eeg_example,
    events = c(120,
               122),
    epoch_labels = c("valid_left",
                     "valid_right"),
    time_lim = c(-.1, .4),
    baseline = c(-.1, 0)
  )

# str(epoched_example)

epoched_example <- electrode_locations(epoched_example)
channels(epoched_example)
topoplot(epoched_example,
         time_lim = c(.22, .24))

eegusta data format: eeg_lst

# devtools::document("C:\\Users\\livio\\OneDrive\\Documenti\\github\\eegusta")
# devtools::install("C:\\Users\\livio\\OneDrive\\Documenti\\github\\eegusta")
# https://github.com/livioivil/eegusta

# devtools::install_github("livioivil/eegusta")
library(eegusta)

eegUtils (eeg_data and eeg_epochs) vs eeguana (eeg_lst)

ex=eegUtils2eeguana(
  epoched_example
)
epoched_example$epochs
# str(ex)
ex$.segments

Example of Group-level data

########### struttura dei dati eeg_data (eegUtils) e eeg_lst (eeguana)
# scaricare:
# https://github.com/angeella/ARIeeg/raw/master/inst/extdata/data_eeg_emotion.RData
# GET("https://github.com/angeella/ARIeeg/raw/master/inst/extdata/data_eeg_emotion.RData",
#     write_disk("./data_eeg_emotion.RData", overwrite = TRUE),
#     progress()
# )

# https://github.com/angeella/ARIeeg/raw/master/inst/extdata/data_eeg_emotion_lst.RData
# GET("https://github.com/angeella/ARIeeg/raw/master/inst/extdata/data_eeg_emotion_lst.RData",
#     write_disk("./data_eeg_emotion_lst.RData", overwrite = TRUE),
#     progress()
# )

library(eegUtils)
load("data_eeg_emotion.RData")
is(dati)
# [1] "eeg_epochs"
# str(dati)

library(eeguana)
load("data_eeg_emotion_lst.RData")
is(data)
# [1] "eeg_lst"
# str(data)

Import Group-level FaCar EEG data

elec_position <- import_chans("canali.ced")
tail(elec_position)
elec_position <- elec_position[-nrow(elec_position),]

D <- read_delim2eeg_data(file="../data-raw/facar_subj_cond_data_preproc/01_CarsERP.txt",
                         chan_info = elec_position)

topoplot(D, time_lim = c(.22, .24))

Dg=eegUtils2eeguana(D)

D$epochs
Dg$.segments


######################
top_dir <- '../data-raw/facar_subj_cond_data_preproc/'
all_files <- list.files(top_dir, full.names = T)
elec_position <- import_chans("canali.ced")

##############
update_ids <- function(newD,refD){
  newD$.signal$.id=newD$.signal$.id+max(refD$.signal$.id)
  newD$.segments$.id=newD$.segments$.id+max(refD$.segments$.id)
  newD$.segments$segment=newD$.segments$segment+max(refD$.segments$segment)
  newD
}


D <- read_delim2eeg_data(file=all_files[1],
                         chan_info = elec_position)
D=eegUtils2eeguana(D)

all_erp=D

for(f_name_set in all_files[-1]){
  D <- read_delim2eeg_data(file=f_name_set,
                           chan_info = elec_position)
  D=eegUtils2eeguana(D)
  D=update_ids(newD=D,refD=all_erp)

  all_erp=bind(all_erp,D)
}

# str(all_erp)


all_erp$.segments$participant_id=gsub("\\.txt","",basename(all_erp$.segments$participant_id))

all_erp$.segments = all_erp$.segments %>% 
  separate(participant_id, c("Subj", "Condition"), "_")

all_erp$.segments$event_type <- all_erp$.segments$epoch_labels <- all_erp$.segments$Condition

all_erp$.segments$Subj=factor(all_erp$.segments$Subj)
all_erp$.segments$event_type=factor(all_erp$.segments$event_type)

save(all_erp,file="../data-raw/all_erp_eeguana.Rdata")


## vedi anche https://bnicenboim.github.io/eeguana/
library(ggplot2)
all_erp %>%
  eeg_select(O1, O2, P7, P8) %>%
  ggplot(aes(x = .time, y = .value)) +
  geom_line(alpha = .1, aes(group = .id, color = Condition)) +
  stat_summary(
    fun = "mean", geom = "line", alpha = 1, size = 1.5,
    aes(color = Condition)
  ) +
  facet_wrap(~.key) +
  geom_vline(xintercept = 0, linetype = "dashed") +
  geom_vline(xintercept = .17, linetype = "dotted") +
  theme(legend.position = "bottom")


all_erp%>%
  eeg_filter(between(as_time(.sample, .unit = "milliseconds"), 140, 180)) %>%
  eeg_group_by(Condition) %>%
  eeg_summarize(across_ch(mean, na.rm = TRUE)) %>%
  plot_topo() +
  annotate_head() +
  geom_contour() +
  geom_text(colour = "black") +
  facet_grid(~Condition)

livioivil/eegusta documentation built on May 6, 2024, 9:20 p.m.