An Application of Spatiotemporal Persistence Landscapes and Dimension Reduction Techniques to EEG Data
Abstract
In this paper, we show an application of spatiotemporal persistence landscapes to real world time series. Spatiotemporal persistence landscapes are a recent extension of persistence landscapes to time series that capture features of the data that are persistent with respect to time and space. We perform our analysis on EEG data to detect absence epileptic seizures. Further, we compare two dimension reduction techniques (DyCA and PCA) with no dimension reduction and show that the combination of DyCA and persistent landscapes yields the best results.
mehrMehr zum Titel
| Titel | An Application of Spatiotemporal Persistence Landscapes and Dimension Reduction Techniques to EEG Data |
|---|---|
| Medien | Proceedings of the 16th APCA International Conference on Automatic Control and Soft Computing (CONTROLO) July 17-19, 2024, Porto, Portugal |
| Verlag | Springer |
| Herausgeber | Aguiar, A.P., Rocha Malonek, P., Pinto, V.H., Fontes, F.A.C.C., Chertovskih, R. |
| Band | 2025, 1325 |
| Verfasser | Martina Flammer |
| Seiten | 308–319 |
| Veröffentlichungsdatum | 23.04.2025 |
| Projekttitel | DyCA |
| Zitation | Flammer, Martina K. (2025): An Application of Spatiotemporal Persistence Landscapes and Dimension Reduction Techniques to EEG Data. Proceedings of the 16th APCA International Conference on Automatic Control and Soft Computing (CONTROLO) July 17-19, 2024, Porto, Portugal 2025, 1325, 308–319. DOI: 10.1007/978-3-031-81724-3_28 |