Merge pull request #74 from craddm/use_new_summary

Use new summary

DESCRIPTION

@@ -5,7 +5,8 @@ Version: 0.6.0
 Date: 2021-01-27
 Authors@R: c(
   person("Matt", "Craddock", role = c("aut", "cre", "cph"), email = "matt@mattcraddock.com"),
-  person("Matti", "Vuorre", role = "ctb", email = "mv2521@columbia.edu"))
+  person("Matti", "Vuorre", role = "ctb", email = "mv2521@columbia.edu"),
+  person("ggplot2 authors", role = c("ctb", "cph"), comment = "Routines borrowed from ggplot2 to allow countours to be drawn on topoplots"))
 Maintainer: Matt Craddock <matt@mattcraddock.com>
 Description: EEG processing and visualization tools. Includes import
   functions for 'BioSemi' (.BDF), 'Neuroscan' (.CNT), 'Brain Vision Analyzer' (.VHDR),
@@ -17,7 +18,7 @@ Encoding: UTF-8
 LazyData: true
 Imports:
     ggplot2,
-    dplyr,
+    dplyr (>= 1.0.0),
     scales,
     mgcv,
     purrr,
@@ -36,7 +37,8 @@ Imports:
     data.table,
     plotly,
     future.apply,
-    Rcpp
+    Rcpp,
+    isoband
 Depends:
   R (>= 3.3.0)
 RoxygenNote: 7.1.1

NAMESPACE

@@ -41,8 +41,10 @@ S3method(compute_csd,eeg_epochs)
 S3method(compute_psd,eeg_data)
 S3method(compute_psd,eeg_epochs)
 S3method(compute_psd,eeg_evoked)
+S3method(compute_psd,eeg_group)
 S3method(compute_tfr,default)
 S3method(compute_tfr,eeg_epochs)
+S3method(compute_tfr,eeg_group)
 S3method(eeg_average,default)
 S3method(eeg_average,eeg_epochs)
 S3method(eeg_average,eeg_evoked)
@@ -119,6 +121,7 @@ S3method(plot_psd,eeg_ICA)
 S3method(plot_psd,eeg_data)
 S3method(plot_psd,eeg_epochs)
 S3method(plot_psd,eeg_evoked)
+S3method(plot_psd,eeg_group)
 S3method(plot_timecourse,data.frame)
 S3method(plot_timecourse,default)
 S3method(plot_timecourse,eeg_ICA)
@@ -176,6 +179,7 @@ S3method(topoplot,eeg_tfr)
 export("channels<-")
 export("epochs<-")
 export("events<-")
+export(StatScalpContours)
 export(StatScalpmap)
 export(apply_ica)
 export(ar_FASTER)
@@ -251,6 +255,7 @@ export(select_elecs)
 export(select_epochs)
 export(select_freqs)
 export(select_times)
+export(stat_scalpcontours)
 export(stat_scalpmap)
 export(tag_epochs)
 export(tag_events)

NEWS.md

@@ -1,9 +1,29 @@
+# eegUtils 0.6.0.9999
+
+### Function changes
+
+- `ar_eogcor` now has a `bipolarize` argument which can be set to false when the HEOG/VEOG channels are already bipolarized.
+- added some new `ggplot2` based functions for topoplotting and adding contours   
+    - `stat_scalpcontours()`
+- `geom_topo()` now has contours
+- added errors with when attempting to use `compute_psd` or `compute_tfr` on `eeg_group` objects.
+
+
+### Internal changes / bug fixes
+
+- `eeg_reference` now handles multiple reference channels better on rereferencing
+- `get_scalpmap` handles `eeg_ICA` components better when there are channels with no locations
+- Travis-CI removed.
+- `cart_to_spherical` coord flipping bug fixed (hopefully...)
+- `filter` now converts to tibble internally and does not coerce `signals` to a vector when there is only one channel.
+- added copyright info to `summary_contour` file
+
 # eegUtils 0.6.0
 
 IMPORTANT: There have been some changes to the logic of the `topoplot()` that may make their appearance quite different. Specifically, these changes are to the way the underlying interpolation grid is calculated and to how things like the diameter of the cartoon head is calculated. These changes often lead to different minimum or maximum amplitudes across the image, and thus changes in the appearance of the plot due to different scales- don't be alarmed!
 
 ### Function changes
-- `epoch_data` baseline correction no defaults to *no* baseline correction
+- `epoch_data` baseline correction now defaults to *no* baseline correction
 - Added `filter` method for `eeg_tfr` objects
 - `fit_glm()` overhauled. Now far faster and allows specification of models using standard R formulae.
 - New `eeg_lm` class introduced for output of `fit_glm()`.

R/artefact_rejection.R

@@ -283,6 +283,7 @@ ar_eogcor <- function(decomp,
 #' @param threshold Threshold for correlation (r). Defaults to NULL,
 #'   automatically determining a threshold.
 #' @param plot Plot correlation coefficient for all components
+#' @param bipolarize Bipolarize the HEOG and VEOG channels?
 #' @describeIn ar_eogcor Method for eeg_ICA objects.
 #' @export
 ar_eogcor.eeg_ICA <- function(decomp,
@@ -291,6 +292,7 @@ ar_eogcor.eeg_ICA <- function(decomp,
                               VEOG,
                               threshold = NULL,
                               plot = TRUE,
+                              bipolarize = TRUE,
                               ...) {
 
   if (!is.null(threshold)) {
@@ -302,9 +304,13 @@ ar_eogcor.eeg_ICA <- function(decomp,
   }
 
   EOG_corrs <- abs(stats::cor(decomp$signals,
-                              bip_EOG(data$signals,
-                                      HEOG,
-                                      VEOG)))
+                              if (bipolarize) {
+                                bip_EOG(data$signals,
+                                        HEOG,
+                                        VEOG)
+                                } else {
+                                  data$signals[,c("HEOG", "VEOG")]
+                                }))
 
   if (is.null(threshold)) {
     mean_corrs <- colMeans(EOG_corrs)

R/channel_management.R

@@ -741,7 +741,7 @@ cart_to_spherical <- function(xyz_coords) {
   phi <- rad2deg(atan(xyz_coords$cart_y / xyz_coords$cart_x))
   theta <- rad2deg(acos(xyz_coords$cart_z / radius))
   theta <- ifelse(xyz_coords$cart_x >= 0, theta, -theta)
-  phi <- ifelse(xyz_coords$cart_x == 0, -phi, phi)
+  #phi <- ifelse(xyz_coords$cart_x == 0, -phi, phi) why is this here?
   phi <- ifelse(is.nan(phi), 0, phi)
   data.frame(radius = 1,
              theta = round(theta),

R/current_source_density.R

@@ -96,9 +96,9 @@ interp_elecs.eeg_data <- function(data,
                              xyz_coords)
 
   data$signals <- interp_chans(data$signals,
-                          bad_elecs,
-                          missing_coords = missing_coords,
-                          weights)
+                               bad_elecs,
+                               missing_coords = missing_coords,
+                               weights)
   data
 }
 

R/data_averaging.R

@@ -47,12 +47,15 @@ eeg_average.eeg_epochs <- function(data,
   data$signals <- dplyr::left_join(cbind(data$signals,
                                          data$timings),
                                    data$epochs, by = "epoch")
-
   if (!is.null(cols)) {
-    if ("participant_id" %in% cols) {
-      col_names <- cols
+    if (identical(cols, "everything")) {
+      col_names <- "participant_id"
     } else {
-      col_names <- c("participant_id", cols)
+      if ("participant_id" %in% cols) {
+        col_names <- cols
+        } else {
+          col_names <- c("participant_id", cols)
+        }
     }
   } else {
     col_names <- names(data$epochs)
@@ -72,8 +75,8 @@ eeg_average.eeg_epochs <- function(data,
 
   epochs <- dplyr::select(timings,
                           epoch,
-                          !!col_names) %>%
-    dplyr::distinct()
+                          !!col_names)
+  epochs <- dplyr::distinct(epochs)
 
   class(epochs) <- c("epoch_info",
                      "tbl_df",

R/data_modifiers.R

@@ -99,7 +99,7 @@ do_referencing <- function(data,
 
     existing_ref <- data$reference$ref_chans
 
-    if (ref_chans %in% existing_ref) {
+    if (any(ref_chans %in% existing_ref)) {
       if (verbose) {
         message(paste0("You have used the existing reference channel(s), ",
                        paste(existing_ref, collapse = " & "),

R/dplyr-extensions.R

@@ -14,7 +14,7 @@ filter.eeg_epochs <- function(.data,
   # labels etc. filter out anything that matches the criteria then return to
   # original format. May want to recode this?
 
-  .data$signals <- as.data.frame(.data)
+  .data$signals <- tibble::as_tibble(.data)
   .data$signals <- dplyr::filter(.data$signals,
                                  ...)
   .data$signals <- .data$signals[, orig_cols]

R/frequency_analysis.R

@@ -130,7 +130,7 @@ compute_psd.eeg_epochs <- function(data,
   } else if (noverlap >= seg_length) {
     stop("noverlap should not be larger than seg_length.")
   }
-  if (method == "Welch") {
+  if (identical(method, "Welch")) {
     final_output <-
       lapply(data$signals,
              function(x) welch_fft(x,
@@ -170,43 +170,57 @@ compute_psd.eeg_evoked <- function(data,
                                    demean = TRUE,
                                    verbose = TRUE,
                                    ...) {
-  if (demean) {
-    data <- rm_baseline(data, verbose = verbose)
-  }
-  srate <- data$srate
-
-  if (is.null(seg_length)) {
-    seg_length <- n_fft
-  }
-
-  if (seg_length > n_fft) {
-    stop("seg_length cannot be greater than n_fft")
-  }
-
-  n_times <- nrow(data$signals)
-  if (n_times < seg_length) {
-    seg_length <- n_times
-  }
-
-  if (is.null(noverlap)) {
-    noverlap <- seg_length %/% 8
-  } else if (noverlap >= seg_length) {
-    stop("noverlap should not be larger than seg_length.")
-  }
-
-    if (method == "Welch") {
-    final_output <-
-      welch_fft(data$signals,
-                seg_length,
-                noverlap = noverlap,
-                n_fft = n_fft,
-                srate = srate,
-                n_sig = n_times)
-  }  else {
-    stop("Welch is the only available method at this time.")
-  }
+  NextMethod("compute_psd", data)
+  # if (demean) {
+  #   data <- rm_baseline(data, verbose = verbose)
+  # }
+  # srate <- data$srate
+  #
+  # if (is.null(seg_length)) {
+  #   seg_length <- n_fft
+  # }
+  #
+  # if (seg_length > n_fft) {
+  #   stop("seg_length cannot be greater than n_fft")
+  # }
+  #
+  # n_times <- nrow(data$signals)
+  # if (n_times < seg_length) {
+  #   seg_length <- n_times
+  # }
+  #
+  # if (is.null(noverlap)) {
+  #   noverlap <- seg_length %/% 8
+  # } else if (noverlap >= seg_length) {
+  #   stop("noverlap should not be larger than seg_length.")
+  # }
+  #
+  #   if (identical(method, "Welch")) {
+  #   final_output <-
+  #     welch_fft(data$signals,
+  #               seg_length,
+  #               noverlap = noverlap,
+  #               n_fft = n_fft,
+  #               srate = srate,
+  #               n_sig = n_times)
+  # }  else {
+  #   stop("Welch is the only available method at this time.")
+  # }
+  #
+  # final_output
+}
 
-  final_output
+#' @noRd
+#' @export
+compute_psd.eeg_group <- function(data,
+                                  seg_length = NULL,
+                                  noverlap = NULL,
+                                  n_fft = 256,
+                                  method = "Welch",
+                                  demean = TRUE,
+                                  verbose = TRUE,
+                                  ...) {
+  stop("Cannot compute psd for `eeg_group` objects.")
 }
 
 #' Welch fft
@@ -335,7 +349,3 @@ split_vec <- function(vec,
   segs
 
 }
-
-
-
-