#' Compute Time-Frequency representation of EEG data
#'
#' This function creates a time frequency representation of EEG time series
#' data. Currently, it is possible to use either Morlet wavelets or Hanning
#' tapers during the decomposition, which uses convolution in the frequency
#' domain.
#'
#' @param data An object of class `eeg_epochs`.
#' @param ... Further TFR parameters
#' @author Matt Craddock \email{matt@@mattcraddock.com}
#' @return An object of class `eeg_tfr`
#' @examples
#' out <- compute_tfr(demo_epochs, method = "morlet", foi = c(4, 30), n_freq = 10, n_cycles = 3)
#' out
#' out$freq_info$morlet_resolution
#' out <- compute_tfr(demo_epochs, method = "morlet", foi = c(4, 30), n_freq = 10, n_cycles = c(3, 10))
#' out$freq_info$morlet_resolution
#' @export
compute_tfr <- function(data, ...) {
UseMethod("compute_tfr", data)
}
#' @describeIn compute_tfr Default method for compute_tfr
#' @export
compute_tfr.default <- function(data, ...) {
warning("compute_tfr requires data in eeg_epochs format.")
}
#' @param method Time-frequency analysis method. Defaults to "morlet".
#' @param foi Frequencies of interest. Scalar or character vector of the lowest
#' and highest frequency to resolve.
#' @param n_freq Number of frequencies to be resolved. Must be an integer number
#' of frequencies.
#' @param n_cycles Number of cycles at each frequency. If a single integer, use
#' a constant number of cycles at each frequency. If a character vector of
#' length 2, the number of cycles will scale with frequency from the minimum
#' to the maximum.
#' @param spacing Use "linear" or "log" spacing for the frequency vector and number of cycles.
#' @param keep_trials Keep single trials or average over them before returning.
#' Defaults to FALSE.
#' @param output Sets whether output is power, phase, or fourier coefficients.
#' @param downsample Downsampling factor. Integer. Selects every n samples after
#' performing time-frequency analysis on the full sampling rate data.
#' @param verbose Print informative messages in console.
#' @describeIn compute_tfr Default method for `compute_tfr`
#' @export
compute_tfr.eeg_epochs <- function(data,
method = "morlet",
foi,
n_freq,
spacing = "linear",
n_cycles = 7,
keep_trials = FALSE,
output = "power",
downsample = 1,
verbose = TRUE,
...) {
if (identical(output, "fourier") && keep_trials == FALSE) {
if (verbose) {
message("For fourier output, all trials are kept.")
}
keep_trials <- TRUE
}
tfr_obj <- switch(
method,
"morlet" = tf_morlet(
data = data,
foi = foi,
n_freq = n_freq,
spacing = spacing,
n_cycles = n_cycles,
keep_trials = keep_trials,
output = output,
downsample = downsample,
verbose = verbose
),
"hanning" = tf_hanning(
data = data,
foi = foi,
n_freq = n_freq,
spacing = spacing,
n_cycles = n_cycles,
keep_trials = keep_trials,
output = output,
downsample = downsample,
verbose = verbose
),
warning(
"Unknown method supplied. Currently supported methods are 'morlet', and 'hanning'"
)
)
tfr_obj
}
#' @describeIn compute_tfr Method for `eeg_evoked` objects.
#' @export
compute_tfr.eeg_evoked <- function(data,
method = "morlet",
foi,
n_freq,
spacing = "linear",
n_cycles = 7,
keep_trials = FALSE,
output = "power",
downsample = 1,
verbose = TRUE,
...) {
if (identical(output, "fourier") && keep_trials == FALSE) {
if (verbose) {
message("For fourier output, all trials are kept.")
}
keep_trials <- TRUE
}
switch(
method,
"morlet" = tf_morlet(
data,
foi = foi,
n_freq = n_freq,
spacing = spacing,
n_cycles = n_cycles,
keep_trials = keep_trials,
output = output,
downsample = downsample,
verbose = verbose
),
"hanning" = tf_hanning(
data = data,
foi = foi,
n_freq = n_freq,
spacing = spacing,
n_cycles = n_cycles,
keep_trials = keep_trials,
output = output,
downsample = downsample,
verbose = verbose
),
warning(
"Unknown method supplied. Currently supported methods are 'morlet' and 'hanning'"
)
)
}
#' @export
compute_tfr.eeg_group <- function(data,
method = "morlet",
foi,
n_freq,
n_cycles = 7,
keep_trials = FALSE,
output = "power",
downsample = 1,
verbose = TRUE,
...) {
stop("Cannot compute tfr for grouped data.")
}
#' Perform Morlet time-frequency analysis
#'
#' Internal function for performing Morlet wavelet transforms using convolution
#' in frequency domain
#'
#' @param data Data in `eeg_epochs` format.
#' @param foi Frequencies of interest. Scalar or character vector of the lowest
#' and highest frequency to resolve.
#' @param n_freq Number of frequencies to be resolved.
#' @param spacing Use linear or log spacing for frequencies.
#' @param n_cycles Number of cycles at each frequency.
#' @param keep_trials Keep single trials or average over them before returning.
#' @param output Sets whether output is power, phase, or fourier coefficients.
#' @param downsample Downsampling factor (integer).
#' @param demean Remove mean before transforming.
#' @param verbose Print informative messages in console.
#' @importFrom abind abind
#' @importFrom stats nextn
#' @keywords internal
tf_morlet <- function(data,
foi,
n_freq,
spacing,
n_cycles,
keep_trials,
output,
downsample,
demean = TRUE,
verbose) {
if (verbose) {
message("Computing TFR using Morlet wavelet convolution")
}
frex <- parse_frex(foi,
n_freq,
verbose,
spacing = spacing)
if (downsample > 1 && verbose) {
message("Downsampling after transformation by a factor of ", downsample)
}
n_freq <- length(unique(frex))
# if a min and max n_cycles is specified, expand out to cycles per n_freq
if (length(n_cycles) == 2) {
if (identical(spacing, "linear")) {
n_cycles <- seq(n_cycles[1],
n_cycles[2],
length.out = n_freq)
} else if (identical(spacing, "log")) {
n_cycles <- exp(
seq(log(n_cycles[1]),
log(n_cycles[2]),
length.out = n_freq)
)
}
} else if (length(n_cycles) > 2) {
stop("n_cycles should be a vector of length 1 or length 2.")
}
#de-mean each epoch
if (demean) {
data <- rm_baseline(data,
verbose = verbose)
}
elecs <- names(data$signals)
#fft_points <- length(unique(data$timings$time))
sigtime <- unique(data$timings$time)
#Create a family of morlet wavelets (unscaled)
morlet_family <- morlet(
frex = frex,
srate = data$srate,
n_freq = n_freq,
n_cycles = n_cycles
)
# Create a list of metadata about the TFR
data$freq_info <- list(
freqs = frex,
morlet_resolution = morlet_res(frex,
n_cycles),
method = "morlet",
output = output,
baseline = "none"
)
n_kern <- nrow(morlet_family)
max_length <- length(unique(data$timings$time))
n_conv <- max_length + n_kern - 1
n_conv <- stats::nextn(n_conv, 2)
# zero-pad and run FFTs on morlets
mf_zp <- fft_n(morlet_family,
n_conv)
# Normalise wavelets for FFT (as suggested by Mike X. Cohen):
norm_mf <- wavelet_norm(mf_zp,
n_freq)
# Run the FFT convolutions on each individual trial
# generate a vector for selection of specific timepoints
time_sel <- seq(1,
length(unique(data$timings$time)),
by = downsample)
data$signals <- run_tf(data,
norm_mf,
n_conv,
n_kern,
keep_trials,
time_sel,
output)
sigtime <- sigtime[time_sel]
data$timings <- data$timings[data$timings$time %in% sigtime,]
if (keep_trials == TRUE) {
dimnames(data$signals) <- list(
epoch = unique(data$timings$epoch),
time = sigtime,
electrode = elecs,
frequency = data$freq_info$freqs
)
} else {
data$signals <- array(data$signals,
dim = c(1, dim(data$signals)))
dimnames(data$signals) <- list(
epoch = "1",
time = sigtime,
electrode = elecs,
frequency = data$freq_info$freqs
)
}
# Remove edges of the TFR'd data, where edge effects would be expected.
edge_mat <- remove_edges(sigtime,
data$freq_info$morlet_resolution$sigma_t)
if (keep_trials == TRUE) {
dims <-
which(names(dimnames(data$signals)) %in% c("time", "frequency"))
data$signals <- sweep(data$signals,
dims,
edge_mat,
"*")
data <- eeg_tfr(
data$signals,
srate = data$srate,
events = data$events,
chan_info = data$chan_info,
reference = data$reference,
timings = data$timings,
freq_info = data$freq_info,
dimensions = names(dimnames(data$signals)),
epochs = data$epochs
)
if (verbose) {
message("Returning single-trial data.")
}
return(data)
}
if (verbose) {
message("Returning signal averaged over all trials.")
}
dims <-
which(names(dimnames(data$signals)) %in% c("time", "frequency"))
data$signals <- sweep(data$signals,
dims,
edge_mat,
"*")
recording_id <- epochs(data)$recording[[1]]
participant_id <- epochs(data)$participant_id[[1]]
data <- eeg_tfr(
data$signals,
srate = data$srate,
events = NULL,
epochs = tibble::tibble(epoch = 1,
recording = recording_id,
participant_id = participant_id),
chan_info = data$chan_info,
reference = data$reference,
timings = unique(data$timings),#data$timings[1:length(sigtime), c("epoch", "time")],
freq_info = data$freq_info,
dimensions = c("epoch",
"time",
"electrode",
"frequency")
)
data
}
#' Morlet wavelet
#'
#' Generate Morlet wavelet family
#'
#' @param frex Frequency range of interest
#' @param srate Sampling rate of signal
#' @param n_cycles Length of wavelet in cycles
#' @param n_freq number of frequencies to resolve
#' @param gauss_width Size of filter kernel in standard deviations
#' @keywords internal
morlet <- function(frex,
srate,
n_cycles,
n_freq,
gauss_width = 3) {
# calculate frequency and temporal std devs
sigma_t <- morlet_res(frex, n_cycles)$sigma_t
max_sd_t <- max(sigma_t)
tstep <- 1 / srate
# round the max SD to the next biggest number divisible by tstep
round_sd <- max_sd_t + (tstep - (max_sd_t %% tstep))
# calculate length of kernel as 3 standard deviations
# TO DO - change this to do it for every frequency separately
wavtime <-
seq(
-round_sd * gauss_width,
round_sd * gauss_width,
by = tstep
)
t_by_f <-
matrix(wavtime,
nrow = length(wavtime),
ncol = length(frex))
# Create sine waves at each frequency
c_sine <- 2 * 1i * pi * sweep(t_by_f,
2,
frex,
"*")
gaussians <- sweep(t_by_f ^ 2,
2,
2 * sigma_t ^ 2,
"/")
m_family <- exp(c_sine - gaussians)
# A <- round_sd^(-1/2) * pi^(-1/4) # Equivalent to Fieldtrip scaling (1/sqrt(sd * sqrt(pi)))
m_family
}
#' Morlet wavelet resolutions
#'
#' Calculate frequency and temporal standard deviations for the Morlet wavelets
#' @param frex Frequencies of interest
#' @param n_cycles Number of cycles for each frequency
#' @keywords internal
morlet_res <- function(frex, n_cycles) {
sigma_f <- frex / n_cycles
sigma_t <- 1 / (2 * pi * sigma_f)
data.frame(frequency = frex,
sigma_f,
sigma_t,
n_cycles)
}
#' N-point FFT
#'
#' Either zero-pads by adding trailing zeroes, or truncates a signal to N and
#' runs an FFT.
#'
#' @param signal signal to be FFT'd
#' @param n Number of FFT points
#' @keywords internal
fft_n <- function(signal, n) {
if (is.vector(signal)) {
if (length(signal) < n) {
signal_n <- c(signal, rep(0, n - length(signal)))
} else {
signal_n <- signal[1:n]
}
stats::fft(signal_n)
} else {
if (nrow(signal) < n) {
signal_n <- matrix(0,
nrow = n,
ncol = ncol(signal))
signal_n[1:nrow(signal), ] <- signal
} else {
signal_n <- signal[1:n, ]
}
stats::mvfft(as.matrix(signal_n))
}
}
#' Remove convolution edges
#'
#' Create a matrix indicating which timepoints likely suffer from edge effects.
#' Returns a time by frequency matrix with NA
#' @param sigtime timepoints in the signal
#' @param sigma_t standard deviations of the morlet wavelets
#' @keywords internal
remove_edges <- function(sigtime, sigma_t) {
#Full width of wavelet kernel is 2 * 3 * sigma_t
sigma_t <- sigma_t * 3
#create a matrix of timepoints for every frequency
times_mat <- matrix(sigtime,
nrow = length(sigtime),
ncol = length(sigma_t))
#calculate time of left edge and replace anything earlier with NA
left_edge <- sigtime[[1]] + sigma_t
times_mat <- t(apply(times_mat,
1,
function(x) ifelse(x < left_edge,
NA,
x)))
#calculate time of right edge and replace anything later with NA
right_edge <- sigtime[[length(sigtime)]] - sigma_t
times_mat <- t(apply(times_mat,
1,
function(x) ifelse(x > right_edge,
NA,
x)))
#Finally, replace anything that isn't NA with 1
times_mat <- ifelse(is.na(times_mat), NA, 1)
times_mat
}
#' Calculate circular mean
#'
#' Calculates the circular mean from vector of phase angles. Input should be in
#' radians.
#'
#' @param data vector of phase angles in radians
#' @keywords internal
circ_mean <- function(data) {
atan(mean(sin(data)) / mean(cos(data)))
}
#' Convert Fourier output to power, phase, or ITC as requested.
#'
#' @param data Fourier coefficients from `eeg_tfr`
#' @param output What output is desired - "power", "phase"
#' @keywords internal
convert_tfr <- function(data,
output) {
if (!is.eeg_tfr(data)) {
stop("This function requires an `eeg_tfr` object as input.")
}
if (is.complex(data$signals[[1]])) {
data$signals <- switch(output,
"power" = abs(data$signals) ^ 2,
"phase" = atan2(Im(data$signals),
Re(data$signals)),
#"itc" = calc_ITC(data),
"fourier" = data$signals)
} else {
warning("Data is not complex, returning original data.")
}
data
}
#' Calculate inter-trial coherence
#'
#' Calculates inter-trial coherence (ITC), a measure of phase consistency across
#' single trial data. Input data must be provided as complex Fourier
#' coefficients within an `eeg_tfr` object
#'
#' @param data An `eeg_tfr` object
#' @return An `eeg_tfr` object
#' @export
compute_itc <- function(data) {
if (!is.eeg_tfr(data)) {
stop("This function requires an `eeg_tfr` object as input.")
}
if (!is.complex(data$signals[[1]])) {
stop("Data is not complex, returning original data.")
}
orig_dimnames <- dimnames(data$signals)
orig_dim <- dim(data$signals)
n_epochs <- orig_dim[1]
if (n_epochs == 1) {
stop("Only one epoch, so ITC cannot be calculated.")
}
data$signals <- data$signals / abs(data$signals)
data$signals <- abs(apply(data$signals,
c(2, 3, 4),
sum))
data$signals <- data$signals / n_epochs
dim(data$signals) <- c(1,
orig_dim[2:4])
dimnames(data$signals) <- c(epoch = list("1"),
orig_dimnames[2:4])
data$timings <- data$timings[data$timings$epoch == 1, ]
epochs(data) <- epochs(data)[epochs(data)$epoch == 1,
c("epoch", "participant_id")]
data$freq_info$output <- "itc"
class(data) <- c("tfr_average",
"eeg_tfr")
data
}
#' Normalise Morlet wavelets
#'
#' Normalise the FFT'd Morlet wavelet family as recommended by Mike X Cohen,
#' dividing each wavelet by its absolute maximum. This should result in each
#' frequency being passed at unit amplitude, and the resulting convolution with
#' the signal should return units approximately on the original scale (i.e. uV^2
#' / Hz). an alternative would be Frobenius norm?
#'
#' @param mf_zp A zero-padded, FFT'd morlet wavelet family
#' @param n_freq Number of frequencies
#' @keywords internal
wavelet_norm <- function(mf_zp, n_freq) {
mf_zp_maxes <- apply(abs(mf_zp),
2,
which.max)
mf_zp_maxes <- lapply(seq_along(mf_zp_maxes),
function(x)
mf_zp[mf_zp_maxes[[x]], x])
norm_mf <- lapply(seq_along(mf_zp_maxes),
function(x)
mf_zp[, x] / mf_zp_maxes[[x]])
norm_mf <- matrix(unlist(norm_mf),
ncol = n_freq)
norm_mf
}
#' @noRd
parse_frex <- function(foi,
n_freq,
verbose,
spacing = NULL) {
if (length(foi) > 2) {
stop("No more than two frequencies should be specified.")
} else if (length(foi) == 2) {
foi <- c(min(foi),
max(foi))
} else {
foi <- c(foi, foi)
n_freq <- 1
}
if (identical(spacing, "log")) {
frex <- exp(
seq(log(foi[1]),
log(foi[2]),
length.out = n_freq)
)
} else {
frex <- seq(foi[1],
foi[2],
length.out = n_freq)
}
if (verbose) {
message(
paste("Output frequencies using", spacing, "spacing:",
paste(round(frex, 2), collapse = " ")
)
)
}
frex
}
#' @noRd
run_tf <- function(tmp,
norm_mf,
n_conv,
n_kern,
keep_trials,
time_sel,
output) {
tmp <- conv_to_mat(tmp)
nhfkn <- floor(n_kern / 2) + 1
n_chans <- dim(tmp)[3]
n_epochs <- dim(tmp)[2]
n_freq <- dim(norm_mf)[2]
n_times <- length(time_sel)
all_times <- nhfkn + (time_sel - 1)
orig_n <- nrow(tmp)
tmp_epo <- array(complex(1),
dim = c(n_conv,
n_epochs))
if (keep_trials) {
if (identical(output, "power")) {
tfr_out <- array(0,
dim = c(n_epochs,
n_times,
n_chans,
n_freq))
for (i in 1:n_chans) {
tmp_epo <- fft_n(tmp[, , i],
n_conv)
for (ik in 1:n_epochs) {
tfr_out[ik, , i, ] <-
abs(stats::mvfft(norm_mf * tmp_epo[, ik],
inverse = TRUE)[all_times, ] / n_conv) ^ 2
}
}
} else {
tfr_out <- array(complex(1),
dim = c(n_epochs,
n_times,
n_chans,
n_freq))
for (i in 1:n_chans) {
tmp_epo <- fft_n(tmp[, , i, drop = FALSE], n_conv)
for (ik in 1:n_epochs) {
tfr_out[ik, , i, ] <-
(stats::mvfft(norm_mf * tmp_epo[, ik],
inverse = TRUE)[all_times,] / n_conv)
}
}
}
} else {
tfr_out <- array(0,
dim = c(n_times, n_chans, n_freq))
for (i in 1:n_chans) {
tmp_epo <- fft_n(tmp[, , i, drop = FALSE],
n_conv)
for (ik in 1:n_epochs) {
tfr_out[, i, ] <-
tfr_out[, i, ] +
abs(stats::mvfft(norm_mf * tmp_epo[, ik], inverse = TRUE)[all_times,] / n_conv) ^ 2
}
}
tfr_out <- tfr_out / n_epochs
}
tfr_out
}
#' Perform Hanning time-frequency analysis
#'
#' Internal function for performing Hanning wavelet transforms using convolution
#' in frequency domain
#'
#' @param data Data in `eeg_epochs` format.
#' @param foi Frequencies of interest. Scalar or character vector of the lowest
#' and highest frequency to resolve.
#' @param n_freq Number of frequencies to be resolved.
#' @param spacing Use linear or log spacing for frequencies.
#' @param n_cycles Number of cycles at each frequency.
#' @param keep_trials Keep single trials or average over them before returning.
#' @param output Sets whether output is power, phase, or fourier coefficients.
#' @param downsample Downsampling factor (integer).
#' @param demean Remove mean before transforming.
#' @param verbose Print informative messages in console.
#' @importFrom abind abind
#' @importFrom stats nextn
#' @keywords internal
tf_hanning <- function(data,
foi,
n_freq,
spacing,
n_cycles,
keep_trials,
output,
downsample,
demean = TRUE,
verbose) {
if (verbose) {
message("Computing TFR using Hanning windows convolution")
}
frex <- parse_frex(foi,
n_freq,
verbose,
spacing = spacing)
if (downsample > 1 && verbose) {
message("Downsampling after transformation by a factor of ", downsample)
}
n_freq <- length(unique(frex))
# if a min and max n_cycles is specified, expand out to cycles per n_freq
if (length(n_cycles) == 2) {
if (identical(spacing, "linear")) {
n_cycles <- seq(n_cycles[1],
n_cycles[2],
length.out = n_freq)
} else if (identical(spacing, "log")) {
n_cycles <- exp(
seq(log(n_cycles[1]),
log(n_cycles[2]),
length.out = n_freq)
)
}
} else if (length(n_cycles) > 2) {
stop("n_cycles should be a vector of length 1 or length 2.")
}
#de-mean each epoch
if (demean) {
data <- rm_baseline(data,
verbose = verbose)
}
elecs <- names(data$signals)
#fft_points <- length(unique(data$timings$time))
sigtime <- unique(data$timings$time)
#Create a family of morlet wavelets (unscaled)
hann_windows <-
hann_family(
frex = frex,
srate = data$srate,
n_cycles = n_cycles
)
# Create a list of metadata about the TFR
data$freq_info <- list(
freqs = frex,
window_resolution = morlet_res(frex,
n_cycles),
morlet_res = "compatibility only, please check window_resolution",
method = "hanning",
output = output,
baseline = "none"
)
#n_kern <- nrow(hann_windows)
max_length <- length(unique(data$timings$time))
n_kern <- length(hann_windows[[1]])
n_conv <- max_length + n_kern - 1
n_conv <- stats::nextn(n_conv, 2)
# zero-pad and run FFTs on morlets
norm_mf <- lapply(
hann_windows,
fft_n,
n = n_conv
)
norm_mf <- matrix(unlist(norm_mf),
ncol = length(norm_mf))
#byrow = TRUE)
# # Normalise wavelets for FFT (as suggested by Mike X. Cohen):
norm_mf <- wavelet_norm(norm_mf,
n_freq)
# Run the FFT convolutions on each individual trial
# generate a vector for selection of specific timepoints
time_sel <- seq(1,
length(unique(data$timings$time)),
by = downsample)
data$signals <- run_tf(data,
norm_mf,
n_conv,
n_kern,
keep_trials,
time_sel,
output)
sigtime <- sigtime[time_sel]
data$timings <- data$timings[data$timings$time %in% sigtime,]
if (keep_trials == TRUE) {
dimnames(data$signals) <- list(
epoch = unique(data$timings$epoch),
time = sigtime,
electrode = elecs,
frequency = data$freq_info$freqs
)
} else {
data$signals <- array(data$signals,
dim = c(1, dim(data$signals)))
dimnames(data$signals) <- list(
epoch = "1",
time = sigtime,
electrode = elecs,
frequency = data$freq_info$freqs
)
}
# Remove edges of the TFR'd data, where edge effects would be expected.
edge_mat <- remove_edges(sigtime,
data$freq_info$window_resolution$sigma_t)
if (keep_trials == TRUE) {
dims <-
which(names(dimnames(data$signals)) %in% c("time", "frequency"))
data$signals <- sweep(data$signals,
dims,
edge_mat,
"*")
data <- eeg_tfr(
data$signals,
srate = data$srate,
events = data$events,
chan_info = data$chan_info,
reference = data$reference,
timings = data$timings,
freq_info = data$freq_info,
dimensions = names(dimnames(data$signals)),
epochs = data$epochs
)
if (verbose) {
message("Returning single-trial data.")
}
return(data)
}
if (verbose) {
message("Returning signal averaged over all trials.")
}
dims <-
which(names(dimnames(data$signals)) %in% c("time", "frequency"))
data$signals <- sweep(data$signals,
dims,
edge_mat,
"*")
recording_id <- epochs(data)$recording[[1]]
participant_id <- epochs(data)$participant_id[[1]]
data <- eeg_tfr(
data$signals,
srate = data$srate,
events = NULL,
epochs = tibble::tibble(epoch = 1,
recording = recording_id,
participant_id = participant_id),
chan_info = data$chan_info,
reference = data$reference,
timings = unique(data$timings),
freq_info = data$freq_info,
dimensions = c("epoch",
"time",
"electrode",
"frequency")
)
data
}
hann_family <- function(frex,
srate,
n_cycles) {
# calculate frequency and temporal std devs
win_sizes <- win_samples(frex = frex,
n_cycles = n_cycles,
srate = srate)
max_win <- max(win_sizes) * 3
windows <- lapply(
seq_along(frex),
function(x) {
current_win <- win_sizes[[x]]
window <- signal::hanning(win_sizes[[x]])
pi_seq <- seq(from = -(current_win - 1) / 2,
to = (current_win - 1)/ 2,
length.out = current_win)
pi_seq <- pi_seq * 2 * pi /srate
prepad <- ceiling((max_win - length(window)) / 2)
postpad <- floor((max_win - length(window)) / 2)
win_times <- pi_seq * frex[[x]]
final_win <- complex(
real = c(rep(0, prepad),
window * cos(win_times),
rep(0, postpad)),
imaginary = c(rep(0, prepad),
window * sin(win_times),
rep(0, postpad))
)
final_win
})
windows
}
win_samples <- function(frex,
n_cycles,
srate) {
samps_sec <- (1 / srate)
cycle_lengths <- 1 / frex
win_lengths <- cycle_lengths * n_cycles # in seconds
ceiling(win_lengths / samps_sec) # round samples up
}
#' Calculate cycles
#'
#' A helper function for calculating the appropriate min-max cycles for a fixed
#' time window/frequency resolution for use with `compute_tfr`. For some
#' analyses you may wish to keep a fixed frequency resolution across the range
#' being analysed, which requires using a fixed time window. `compute_tfr`
#' expects the minimum and maximum number of cycles to be supplied. Use this
#' function to calculate the equivalent number of cycles at each frequency.
#'
#' @param time_win Time window in seconds.
#' @param frex Frequencies of interest.
#' @export
#' @return the number of cycles for each frequency of interest
#' @examples
#' cycle_calc(.5, seq(3, 30, length.out = 10))
#' no_scale_tfr <- compute_tfr(demo_epochs, foi = c(3, 30),
#' n_cycles = range(cycle_calc(0.5, seq(3, 30, length.out = 10))),
#' n_freq = 10)
#' @seealso \code{\link{compute_tfr}}
cycle_calc <- function(time_win,
frex) {
time_win * frex
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.