Skip to main content

SOUND & SSP-SIR algorithms for TMS-EEG artifact cleaning (MNE + MATLAB Engine)

Project description

pytep — Python TMS-EEG Processing

SOUND and SSP-SIR algorithms for TMS-EEG artifact cleaning, bridging MNE-Python and MATLAB Engine.

Requirements

  • Python ≥ 3.11
  • MATLAB (with a valid license)
  • MATLAB Engine for Python (matlabengine)
  • MNE-Python

Installation

# From the repository root
pip install -e .

# With notebook extras (jupyter, matplotlib, etc.)
pip install -e ".[notebooks]"

Quick Start

SOUND (Mutanen et al., 2018)

import mne
from pytep import apply_sound

# Load your data
raw = mne.io.read_raw_edf("your_data.edf", preload=True)
raw.set_montage("standard_1020")

# Apply SOUND
raw_clean = apply_sound(raw, iter_num=5, lambda_val=0.1)

SSP-SIR (Mutanen et al., 2016)

import mne
from pytep import apply_sspsir

# Load epoched data
epochs = mne.read_epochs("your_epochs-epo.fif", preload=True)

# Apply SSP-SIR
epochs_clean = apply_sspsir(epochs, pc=2, art_scale='automatic')

Sharing a MATLAB Engine session

import matlab.engine
from pytep import apply_sound, apply_sspsir

eng = matlab.engine.start_matlab()

raw_clean = apply_sound(raw, eng=eng)
epochs_clean = apply_sspsir(epochs, eng=eng, pc=2)

eng.quit()

API Reference

apply_sound(inst, eng=None, iter_num=5, lambda_val=0.1)

Parameter Type Description
inst Raw/Epochs MNE object with EEG data (montage required)
eng MatlabEngine Existing MATLAB session (optional)
iter_num int Number of SOUND iterations (default: 5)
lambda_val float Regularization parameter (default: 0.1)

apply_sspsir(inst, eng=None, art_scale='automatic', time_range=None, pc=1, M=None)

Parameter Type Description
inst Epochs Epoched MNE data (montage required)
eng MatlabEngine Existing MATLAB session (optional)
art_scale str 'automatic', 'manual', or 'manualConstant'
time_range list[float] [start_ms, end_ms] artifact window
pc int Number of artifact PCs to remove (default: 1)
M int/None SIR truncation dimension

References

  • SOUND: Mutanen, T. P., et al. (2018). Automatic and robust noise suppression in EEG and MEG: The SOUND algorithm. NeuroImage, 166, 135-151.
  • SSP-SIR: Mutanen, T. P., et al. (2016). Recovering TMS-evoked EEG responses masked by muscle artifacts. NeuroImage, 139, 157-166.

License

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pytep_sound_sspsir-0.1.3.tar.gz (213.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pytep_sound_sspsir-0.1.3-py3-none-any.whl (34.4 kB view details)

Uploaded Python 3

File details

Details for the file pytep_sound_sspsir-0.1.3.tar.gz.

File metadata

  • Download URL: pytep_sound_sspsir-0.1.3.tar.gz
  • Upload date:
  • Size: 213.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.14

File hashes

Hashes for pytep_sound_sspsir-0.1.3.tar.gz
Algorithm Hash digest
SHA256 43b70f2edf3714abca82fcbf44af4ba20c48ef8ae413762f8a19cc9b8da23bbc
MD5 045c8258ab723cb2256419013892ad8c
BLAKE2b-256 ca0fc1c3c26797d6721371835608e1d4cd231f3b2cab3519056dced1598d81bd

See more details on using hashes here.

File details

Details for the file pytep_sound_sspsir-0.1.3-py3-none-any.whl.

File metadata

File hashes

Hashes for pytep_sound_sspsir-0.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 397bc194296d466c855ca500edc5ec969603ec80acebcd439b6baa5d7efc9eed
MD5 ea79d4dbf4457589d94eda383ea58e40
BLAKE2b-256 9e0b9ec63a413c31abe10d8267f986776272f93b802f4490e054b2335d715c93

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page