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.

Mehr zum Titel

Titel An Application of Spatiotemporal Persistence Landscapes and Dimension Reduction Techniques to EEG Data
Medien Vortrag auf APCA International Conference on Automatic Control and Soft Computing (CONTROLO 2024)
Band 2024
Verfasser M Flammer
Veröffentlichungsdatum 18.07.2024
Projekttitel DyCA
Zitation Flammer, M (2024): An Application of Spatiotemporal Persistence Landscapes and Dimension Reduction Techniques to EEG Data. Vortrag auf APCA International Conference on Automatic Control and Soft Computing (CONTROLO 2024) 2024.