README

The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

ravepipeline

The goal of ravepipeline is to provide pipeline infrastructure for Reproducible Analysis and Visualization of Intracranial Electroencephalography (RAVE). The package defines high-level class to build, compile, set, execute, and share neuroscience pipelines. Both R and Python are supported with Markdown and shiny dashboard templates for extending and building customized pipelines.

Installation

# install.packages(c("remotes", "visNetwork", "rpymat"))

# Install development version
remotes::install_github("dipterix/ravepipeline")

# Download built-in pipelines
ravepipeline::ravepipeline_finalize_installation()

# Configure Python environment if needed
rpymat::configure_conda()

Run analysis pipelines

This example requires you to download RAVE demo data, which comes with the full installations.

The built-in pipelines include Power Explorer, a powerful tool to group stimuli for time-frequency analysis and visualization. Here’s an example of how to use it on our demo data set:

library(ravepipeline)

# list all the pipelines
pipeline_list()

# Run power explorer
power_explorer <- pipeline("power_explorer")

# List all runnable pipeline targets
power_explorer$target_table

# set inputs for analysis
power_explorer$set_settings(
  project_name = "demo", 
  subject_code = "DemoSubject", 
  loaded_electrodes = "13-16,24", 
  epoch_choice = "auditory_onset", 
  epoch_choice__trial_starts = -1L, 
  epoch_choice__trial_ends = 2L, 
  reference_name = "default", 
  baseline_settings = list(
    window = list(c(-1, -0.5)),
    scope = "Per frequency, trial, and electrode", 
    unit_of_analysis = "decibel"), 
  analysis_electrodes = "14", 
  first_condition_groupings = list(
    list(label = "audio_visual", conditions = c("known_av", "meant_av", "last_av", "drive_av")),
    list(label = "auditory_only", conditions = c("last_a", "drive_a", "known_a", "meant_a")), 
    list(label = "visual_only", conditions = c("last_v", "drive_v", "known_v", "meant_v"))), 
  condition_variable = "Condition", 
  analysis_settings = list(
    list(label = "AudStart", event = "Trial Onset", 
         time = 0:1, frequency = c(70L, 150L))),
  enable_second_condition_groupings = FALSE, 
  enable_custom_ROI = FALSE, 
  omnibus_includes_all_electrodes = TRUE
)


# Run pipeline to obtain the power of frequency over time
time_freq_data <- power_explorer$run("by_frequency_over_time_data")

# Load up custom pipeline functions
pipeline_functions <- power_explorer$shared_env()

# plot the result
pipeline_functions$plot_by_frequency_over_time(time_freq_data)

# visualize the results
power_explorer$visualize(glimpse = TRUE, aspect_ratio = 10)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.