Author: Jules GOMEL
Year: 2025
This repository contains code to practice SSVEP (Steady-State Visually Evoked Potential) signal-to-noise ratio (SNR) computation based on EEG data from Ladouce et al. (2022):
Improving user experience of SSVEP BCI through low amplitude depth and high frequency stimuli design
Zenodo Dataset
The SNR computation methodology follows the approach proposed by Cohen & Gulbinaite (2017):
Rhythmic Entrainment Source Separation: Optimizing analyses of neural responses to rhythmic sensory stimulation
Paper Link (DOI)
The signal-to-noise ratio (SNR) is calculated by comparing the power at a target frequency to the average power of neighboring frequencies, excluding a small band around the target to avoid signal contamination. This method is particularly useful for analyzing rhythmic entrainment in EEG/BCI studies.
- Python 3.x
- NumPy
- SciPy
- Matplotlib (optional for plotting)
- MNE (optional if working directly with EEG files)
- Requirements
- environment file in yaml