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-0.1.2.tar.gz
(203.4 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file pytep_sound_sspsir-0.1.2.tar.gz.
File metadata
- Download URL: pytep_sound_sspsir-0.1.2.tar.gz
- Upload date:
- Size: 203.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.11.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
2fa2d24701e1bf986603b67e12561e46e669448a59437fd0da9353a806e95f74
|
|
| MD5 |
232ff803dcda6263120f2bb4c43a68fd
|
|
| BLAKE2b-256 |
745ab5b1594b8d26b09dd7746bf88be6ff4a8c3fc2bbd69b82d0244bc3913e05
|
File details
Details for the file pytep_sound_sspsir-0.1.2-py3-none-any.whl.
File metadata
- Download URL: pytep_sound_sspsir-0.1.2-py3-none-any.whl
- Upload date:
- Size: 34.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.11.14
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
b66e6f5e7bc451ae662a9af38679c641da571f251c658fa9f9842be386dde85a
|
|
| MD5 |
1663fb2c0260635ca06fb6a841a801ba
|
|
| BLAKE2b-256 |
088b402ad60572e7c209c179488df24367ef0aa17eb9ec1fe2553c03c5090e9e
|