chaklam-silpasuwanchai/EEG-Emotion-Recognition
This repository compares typical and advanced modeling approaches for EEG Emotion Recognition.
This project helps EEG researchers understand how different emotion recognition models perform under controlled conditions. It takes raw or preprocessed EEG data and outputs a comparison of various traditional and deep learning algorithms, showing what works best for identifying emotions. An EEG researcher would use this to inform their architectural decisions for emotion recognition studies.
No commits in the last 6 months.
Use this if you are an EEG researcher or student looking for a standardized comparison of emotion recognition models using the DEAP dataset.
Not ideal if you need a plug-and-play solution for real-time emotion detection without any research or development.
Stars
30
Forks
4
Language
Jupyter Notebook
License
—
Category
Last pushed
Sep 18, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/chaklam-silpasuwanchai/EEG-Emotion-Recognition"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
mne-tools/mne-python
MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
braindecode/braindecode
Deep learning software to decode EEG, ECG or MEG signals
NeuroTechX/moabb
Mother of All BCI Benchmarks
neuromodulation/py_neuromodulation
Real-time analysis of intracranial neurophysiology recordings.
IoBT-VISTEC/MIN2Net
End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification (IEEE...