Advanced EEG automated labeling for seizure detection
The EEG Analysis Platform is a web-based tool designed to showcase EEG labeling system. This platform enables researchers and clinicians to visualize electroencephalography signals in bilateral montage and leverage machine learning models for automated event detection and classification.
Load entire directories containing EDF (European Data Format) files for batch processing and analysis.
Import labels via CSV files with the same name as EDF files. Columns: channel, start_time, stop_time, label, confidence.
View EEG signals in standard bilateral montage with clear, interactive visualization of all channels.
Utilize trained machine learning models to automatically predict and label events in unlabeled EEG segments.
Seamlessly visualize predicted and manual labels alongside the raw EEG signal for easy comparison and validation.
Results are cached to improve performance and reduce redundant computations.
This platform serves as a demonstration tool for EEG labeling system. It showcases the integration of signal processing techniques with machine learning models trained on large-scale clinical datasets, aiming to improve the accuracy and efficiency of seizure detection and EEG event classification.
We welcome collaboration opportunities, feedback, and discussions about EEG analysis and machine learning applications in neurophysiology.
📧 Contact us at: info@palmaf.com
The machine learning models implemented in this platform were trained using data from the following datasets. We gratefully acknowledge the contributions of these research teams:
Shah, V., von Weltin, E., Lopez. S., McHugh, J., Veloso, L., Golmohammadi, M., Obeid, I., and Picone, J. (2018). The Temple University Hospital Seizure Detection Corpus. Frontiers in Neuroinformatics. 12:83. doi: 10.3389/fninf.2018.00083
Harati, A., Golmohammadi, M., Lopez, S., Obeid, I., & Picone, J. (2015). Improved EEG Event Classification Using Differential Energy. Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (pp. 1-4). Philadelphia, Pennsylvania, USA.
Buckwalter, G., Chhin, S., Rahman, S., Obeid, I., & Picone, J. (2021). Recent Advances in the TUH EEG Corpus: Improving the Interrater Agreement for Artifacts and Epileptiform Events. In I. Obeid, I. Selesnick, & J. Picone (Eds.), Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB) (pp. 1–3). IEEE. https://doi.org/10.1109/SPMB52430.2021.9672302