Metadata-Version: 2.2
Name: spyeeg
Version: 0.2.2
Summary: Package for modelling electrophysiological responses to stimuli
Author-email: Pierre Hieu Guilleminot <pierre.hieu.guilleminot@gmail.com>, Mikolaj Kegler <mikolajkegler@gmail.com>, Michael Thornton <m.thornton20@imperial.ac.uk>
License: BSD 3
Project-URL: Repository, https://github.com/phg17/sPyEEG
Requires-Python: >3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: mne
Requires-Dist: scikit-learn
Requires-Dist: colorednoise
Requires-Dist: joblib
Requires-Dist: psutil
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: reservoirpy
Requires-Dist: netCDF4
Requires-Dist: h5netcdf
Requires-Dist: PyQt5

# sPyEEG

#### Version: 0.0.1

Package for modelling s/M/EEG responses to speech. In other words, for mapping speech features, through python (*sPyeech*) to EEG (*sPyEEG*)... and the other way around! 

Not *mind-reading* for espionage purposes ;).

### Setup

#### Requirements
Package builds on top on [MNE](https://mne.tools/stable/index.html) and relies on a similar set of dependencies and 3rd party packages listed in ```environment.yml```. You can easily set up the environment via [Conda](https://docs.conda.io/en/latest/) package manager by running in terminal From terminal (or _conda shell_ on Windows): 
```bash
conda env update --file environment.yml
```
Then activate the created environment by running:
```bash
conda activate spyeeg
```

#### Installation
To get the package installed only through symbolic links, namely so that you can modify the source code and use modified versions at will when importing the package in your python scripts do:

```bash
python setup.py develop
```

Otherwise, for a standard installation (but this will require to be installed if you need to install another version of the library):

```bash
python setup.py install
```

##### Tested on:
- macOS Big Sur v11.1
- Ubuntu 18.04.5 LTS
- Windows 10 22H2

### Modules (sketch)
- **models** - for all your modelling needs
  - TRF: Temporal Response Function a.k.a Ridge regression a.k.a. fancy linear regression, optimized for speed
  - iRRR: integrative reduced rank regression a.k.a fancier linear regression
  - _methods: useful methods used by several model classes
  - CCA/Decoder/ERP: to be properly written at a later date.
- **feat** - simple feature extraction.
- **preproc** - useful preprocessing functions (filters, detrending...etc)
- **viz** - visualization tools. To come.
- **utils** - misc.

### Examples
**Note**: Sample data required for demos can be downloaded [here](https://imperialcollegelondon.box.com/s/afalp7tysg6nlayb5hftyn5xopv6uh99). When downloaded place the files in the ```demos/Data``` folder.
- **Modelling** (```models```)
  - Basic TRF modelling: ```demo/Demo_TRF.py```
  - iRRR demo: coming soon
- **Feature extraction** (```feat```)
  - Speech envelope extraction: ```demo/Demo_envelopes.py```


#### Contributors:
- Pierre Guilleminot (phg17@ic.ac.uk)
- Mikolaj Kegler (mak616@ic.ac.uk)

Last updated: 15th Apr 2024
