# Import MNE-BIDS processing from mne_bids import BIDSPath, read_raw_bids, get_entity_vals # %% # Set up directories # ------------------ # # First we will define where the raw data is stored. We will analyse a # BIDS dataset, note that the BIDS specification for NIRS data is still # under development and you will need to install the development branch # as described above. # # We first define the root directory of our dataset. root = fnirs_motor_group.data_path() print(root) # %% # And as we are using MNE-BIDS we can create a BIDSPath. # This helps to handle all the path wrangling. dataset = BIDSPath(root=root, task="tapping") print(dataset.directory) # %% # For example we can automatically query the subjects, tasks, and sessions. subjects = get_entity_vals(root, 'subject') print(subjects)
# Set up directories
# ------------------
# .. sidebar:: Requires MNE-BIDS fNIRS branch
#
# This section of code requires the MNE-BIDS fNIRS branch.
# See instructions at the top of the page on how to install.
# Alternatively, if your data is not in BIDS format,
# skip to the next section.
#
# First we will define where the raw data is stored. We will analyse a
# BIDS dataset. This ensures we have all the metadata we require
# without manually specifying the trigger names etc.
# We first define where the root directory of our dataset is.
# In this example we use the example dataset ``audio_or_visual_speech``.
root = data_path()
dataset = BIDSPath(root=root,
suffix="nirs",
extension=".snirf",
task="tapping",
datatype="nirs")
subjects = get_entity_vals(root, 'subject')
# %%
# Define individual analysis
# --------------------------
#
# More details on the epoching analysis can be found
# at :ref:`Waveform individual analysis <tut-fnirs-processing>`.
# A minimal processing pipeline is demonstrated here, as the focus
# of this tutorial is to demonstrate the decoding pipeline.