osmanberke/Ensemble-of-DNNs
Official Repository of 'Transfer learning of an ensemble of DNNs for SSVEP BCI spellers without user-specific training'
This project helps researchers and engineers quickly set up and test Brain-Computer Interface (BCI) spellers that use Steady-State Visual Evoked Potentials (SSVEP). It takes raw SSVEP signal data as input and produces classifications of user intent without needing extensive user-specific calibration. Neurotechnology developers and BCI researchers can use this to evaluate new BCI speller systems efficiently.
No commits in the last 6 months.
Use this if you are developing or evaluating SSVEP-based BCI spellers and want to reduce the need for individual user training data.
Not ideal if you are working with BCI paradigms other than SSVEP, or if you need to perform real-time, online BCI control directly.
Stars
26
Forks
5
Language
MATLAB
License
GPL-3.0
Category
Last pushed
Sep 22, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/osmanberke/Ensemble-of-DNNs"
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...