neuropipeline 0.2.4

A tool for quick and easy preprocessing and visualization of fNIRS data

neuropipeline

neuropipeline is a tool for quick and easy to use preprocessing and visualization of Functional Near-Infrared Spectroscopy (fNIRS) data.

Usage

from neuropipeline import fNIRS
import neuropipeline.fnirs.visualizer as nplv

fnirs = fNIRS("path/to/your_data.snirf")
fnirs.preprocess(optical_density=True,
                 hemoglobin_concentration=True,
                 motion_correction=True,
                 temporal_filtering=True,
                 detrending=True,
                 normalization=False # Only use normalization if necessary
                 )

# WARNING: Be cautious not to overwrite any data you want to keep.
fnirs.write_snirf("path/to/your/new_processed_data.snirf") 

nplv.open(fnirs)

Analysis Example: Heel Stimulation

These plots display data from a single subject during a robotic heel-stimulation experiment, showing the Time Series, Spectrogram, and Frequency (PSD/FFT) for two different scenarios. The vertical red dashed lines indicate "markers," which show exactly when a task started or when the robot moved. In this experiment, a robot stimulated the heel 6 times. In the Supination case (left), we see a clear success: oxygenated hemoglobin (HbO) increases right when the stimuli begin. This is supported by the spectrogram, where we see "spikes" of activity at 0.025 Hz (the neurogenic band) that align perfectly with the robot's movements. This confirms the pipeline has successfully captured brain activity in the sensory cortex. In contrast, the Pronation case (right) shows consistently low activity in the spectrogram, and while the time series has some small peaks, they do not show the same clear correlation with the stimulation.

Supination	Pronation

Installation

python -m pip install neuropipeline

Advanced Usage

from neuropipeline import fNIRS, fNIRSPreprocessor
from neuropipeline.fnirs import visualizer as nplv

fnirs = fNIRS("path/to/your_file.snirf")

# Advanced Preprocessing Configuration
pp = fNIRSPreprocessor(fnirs) # Create preprocesssor
pp.set_optical_density(True) # Configure
pp.set_hemoglobin_concentration(True)
pp.set_motion_correction(True)
pp.set_temporal_filtering(True, lowcut=0.01, highcut=0.2, order=15)
pp.set_detrending(True)
pp.set_normalization(False)

pp.print() # Inspect the settings

fnirs.preprocess(pp) # Pass the preprocesser only

fnirs.write_snirf("path/to/your_new_file.snirf") # WARNING : Dont overwrite data you want to keep

nplv.set_spectrogram_limits(0.0, 0.2) # The spectrogram will show frequencies from  0 to 0.2 Hz 

nplv.set_marker_dictionary({2:"Rest",      # Display as text rather than indices
                             3:"Stimuli A", 
                             4:"Stimuli B"})

nplv.set_spectrum_mode("FFT") # What type of spectrum to show: "FFT" or "PSD"

# NOTE : The wavelet method is computationally intensive
# Try "STFT" first, then "Wavelet" if needed
nplv.set_spectrogram_method("Wavelet") 

nplv.open(fnirs)