vignettes/plot_compute_evoked_in_r.R

In mne-tools/mne-r: Fast Access to MNE-Python From Within R

## ----setup, include = T, echo = F----------------------------------------
library(tidyverse)
library(mne)

## ------------------------------------------------------------------------
data_path <- mne$datasets$sample$data_path()
subject <- "sample"

raw_fname <- paste(data_path,
                   'MEG', 
                   subject,
                   'sample_audvis_filt-0-40_raw.fif',
                   sep = '/')

raw <- mne$io$read_raw_fif(raw_fname, preload = T)
raw$filter(1, 40)

events <- mne$find_events(raw)
storage.mode(events) <- "integer"  # R gets the events as floats.

tmin <- -0.2
tmax <- 0.5
baseline <- reticulate::tuple(NULL, 0)
event_id <- list("aud/l" = 1L, "aud/r" = 2L,
                 "vis/l" = 3L, "vis/r" = 4L)
picks <- mne$pick_types(raw$info, meg = T, eeg = T)
epochs <- mne$Epochs(raw = raw, events = events, event_id =event_id,
                     tmin = tmin, tmax = tmax,
                     picks = picks %>% as.integer(),
                     baseline = baseline, reject = NULL, preload = T) 

## ------------------------------------------------------------------------
# use MNE-R function in wide format
epochs_df <- mne::get_data_frame(epochs, long_format = F)

# get rid of info variables: "condition", "epoch", "time"  
ch_names <- colnames(epochs_df)[-c(1:3)]

# modify .funs to estimate the average differently.
evoked_df <- epochs_df %>%
  group_by(time, condition) %>%
  summarize_at(.funs = mean, .vars = ch_names) %>% 
  gather(key = "channel", value = "signal", -time, -condition)

## ---- fig.width=7, fig.height=5------------------------------------------
ggplot(
  data = evoked_df %>% filter(str_detect(channel, "EEG")),
  mapping = aes(x = time, y = signal,
                group = interaction(channel, condition),
                color = condition)) +
  geom_line(alpha = 0.6, size = 1.5) +
  scale_color_brewer(palette="RdYlBu") +
  theme_minimal() +
  theme(text = element_text(size = 30, family = "Helvetica"),
        legend.position = "top")+
  labs(y = "EEG [mV]", x = "time [ms]", color = "")