library(dplyr)
library(eeguana)
library(httr)
GET(
"https://osf.io/q6b7x//?action=download",
write_disk("./faces.vhdr", overwrite = TRUE),
progress()
)
GET(
"https://osf.io/ft5ge//?action=download",
write_disk("./faces.vmrk", overwrite = TRUE),
progress()
)
GET(
"https://osf.io/85dgj//?action=download",
write_disk("./faces.dat", overwrite = TRUE),
progress()
)
faces <- read_vhdr("faces.vhdr")
channels_tbl(faces) <- select(channels_tbl(faces), .channel) %>%
left_join(layout_32_1020)
data_faces_ERPs <- faces %>%
eeg_segment(.description %in% c("s70", "s71"),
.lim = c(-.2, .25)
) %>%
eeg_events_to_NA(.type == "Bad Interval") %>%
eeg_baseline() %>%
mutate(
condition =
if_else(description == "s70", "faces", "non-faces")
) %>%
select(-type) %>%
group_by(.sample, condition, .recording) %>%
summarize_at(channel_names(.), mean, na.rm = TRUE) %>%
ungroup()
pos_10 <- events_tbl(faces) %>%
filter(.type == "Stimulus", .description == "s130") %>%
pull(.initial) %>%
.[10]
data_faces_10_trials <- faces %>%
filter(.sample %>% between(15000, pos_10)) %>%
ungroup()
usethis::use_data(data_faces_ERPs, data_faces_10_trials, overwrite = TRUE, compress = "xz")
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