Toolbox for automatic detection and localization of interictal events in MEG/EEG
- Automatic spike detection based on mimetic approach and the constrained parametric morphological model
- Robust against noisy high-amplitude transients
- Adaptable to different scales of the patterns
src/fpcm_detector.py— Core algorithm for spike detection using FPCMsrc/summary.py— Convenient visualization tools of the results (e.g., topographies of the spikes, overlays with the splines, etc.)src/source_loc.py— Dipole fitting of the detected spikessrc/utils.py— Auxiliary tools for evaluating performance, etc.
An example script demonstrating the use of the FPCM algorithm is available in scripts/FPCM demo.ipynb
You can download the simulated MEG dataset with interictal spikes used in this script from Google Drive.
If you use this code, please cite the following paper:
Kleeva, D., Soghoyan, G., Komoltsev, I., Sinkin, M., & Ossadtchi, A. (2022). Fast parametric curve matching (FPCM) for automatic spike detection. Journal of Neural Engineering, 19(3), 036003.
This project is licensed under the MIT License.