Metadata-Version: 2.4
Name: eegemolib
Version: 0.1.1.post2
Summary: Unified Evaluation Framework for EEG-Based Emotion Recognition Algorithms
Project-URL: Homepage, https://github.com/Schmerle-Cki/EEGEmoLib
Project-URL: Issues, https://github.com/Schmerle-Cki/EEGEmoLib/issues
Author-email: Yezhi Shu <shuyz19@tsinghua.org.cn>
License-Expression: MIT
License-File: LICENSE
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Requires-Dist: matplotlib
Requires-Dist: mne
Requires-Dist: numpy
Requires-Dist: pyyaml
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: torch
Requires-Dist: torchvision
Requires-Dist: tqdm
Description-Content-Type: text/markdown

# EEGEmoLib

EEGEmoLib is a plug-and-play EEG toolkit for EEG-based emotion analysis. It streamlines EEG emotion recognition workflows by combining dataset management, feature engineering, model training, and visualization utilities in one practical framework.

## Installation

```bash
pip install eegemolib
```

## Core Modules

- Datasets: configurable dataset loading through a unified YAML-based workflow
- Feature extraction: 17 handcrafted EEG features, including coherence
- Feature selection: 11 methods across filter, wrapper, and embedding strategies
- Recognition models: 15 representative EEG emotion recognition models
- Visualization: tools for inspecting EEG signals, feature distributions, and model behavior

## Quick Start

From the repository root:

```bash
python -m eegemolib.engine.protocol --cfg src/eegemolib/cfg/experiments/seed_protocols/cross_session/deepconvnet_psd.yaml
```

Before running, update `dataset.data_root` in the experiment config to point to your local dataset directory.

If you want to run directly from source without installing the package, use:

```bash
PYTHONPATH=src python -m eegemolib.engine.protocol --cfg src/eegemolib/cfg/experiments/seed_protocols/cross_session/deepconvnet_psd.yaml
```

## Resources

- Homepage: https://eegemolib.github.io
- Documentation: https://eegemolib-docs.readthedocs.io/en/latest/index.html#
- PyPI: https://pypi.org/project/eegemolib/

The homepage provides a concise visual overview of EEGEmoLib, while the documentation site contains detailed module references.

