# Dynamical Component Analysis (DyCA) and its application on epileptic EEG

## TL;DR

Dynamical Component Analysis (DyCA) is a new method for reducing dimensionality in deterministic datasets, applied here to epileptic EEG data for seizure detection, showing promising results compared to PCA and ICA.

## Contribution

DyCA introduces a novel eigenvalue-based approach for dimensionality reduction and seizure detection in EEG data, offering a straightforward implementation and improved detection metrics.

## Key findings

- DyCA effectively reduces EEG data dimensionality.
- Eigenvalues of DyCA can be used for seizure detection.
- DyCA outperforms PCA and ICA in specificity and false discovery rate.

## Abstract

Dynamical Component Analysis (DyCA) is a recently-proposed method to detect projection vectors to reduce the dimensionality of multi-variate deterministic datasets. It is based on the solution of a generalized eigenvalue problem and therefore straight forward to implement. DyCA is introduced and applied to EEG data of epileptic seizures. The obtained eigenvectors are used to project the signal and the corresponding trajectories in phase space are compared with PCA and ICA-projections. The eigenvalues of DyCA are utilized for seizure detection and the obtained results in terms of specificity, false discovery rate and miss rate are compared to other seizure detection algorithms.