yasa_classifier

Repository to reproduce training example using yasa and an open source data set

Dataset Origin

The dataset was collected from OSF. It contains mouse EEG/EMG recordings (sampling rate: 512 Hz) and sleep stage labels (epoch length: 2.5 sec).

Training was performed using extracted features from 24h recordings.

Procedure to reproduce

Dataset was downloaded manually and saved in the folder same folder as code using this structure:

Dataset structure

Datasets have the following structure

.
├── Mouse01
│   ├── Day1_dark_cycle
│   │   ├── EEG.mat
│   │   ├── EMG.mat
│   │   └── labels.mat
│   ├── Day1_light_cycle
│   │   ├── EEG.mat
│   │   ├── EMG.mat
│   │   └── labels.mat
│   ├── Day2_dark_cycle
│   │   ├── EEG.mat
│   │   ├── EMG.mat
│   │   └── labels.mat
│   └── Day2_light_cycle
│       ├── EEG.mat
│       ├── EMG.mat
│       └── labels.mat

Extract Features

01-extract_features.qmd was run to extract features. An important note is that it uses a local version of SleepStaging() (from staging import SleepStaging) that differs from the implementation in yasa. This was included for reproducibility, though we have plans to include this version in yasa itself and will be no longer needed.

update1: resample mouse EEG/EMG recordings to 100 Hz for training to save time.

update2: add extra features in staging.py, such as power ratios of EEG and SVD entropy.

Train the model

02-train.qmd was run to train on the 24 hour recordings. The outputs of this notebook are saved into /output.

Evaluate the model

03-evaluate.qmd was run to evaluate and produce accuracy and cohen's kappa metrics. The outputs of this notebook are saved into /output.

Contribute

This is a preliminary release, file issues to enhance functionality.

mitometa/yasa_classifier