# Correntropy Based Robust Decomposition of Neuromodulations

## TL;DR

This paper introduces a noniterative, correntropy-based algorithm for robustly isolating neuromodulations in EEG signals, outperforming existing methods in efficiency while maintaining high accuracy.

## Contribution

The paper presents a novel, noniterative classification algorithm leveraging correntropy for EEG neuromodulation detection, reducing computational complexity compared to state-of-the-art methods.

## Key findings

- Matches performance of state-of-the-art techniques
- Significantly reduces computation time and complexity
- Effective in isolating neuromodulations in EEG data

## Abstract

Neuromodulations as observed in the extracellular electrical potential recordings obtained from Electroencephalograms (EEG) manifest as organized, transient patterns that differ statistically from their featureless noisy background. Leveraging on this statistical dissimilarity, we propose a noniterative robust classification algorithm to isolate, in time, these neuromodulations from the temporally disorganized but structured background activity while simultaneously incorporating temporal sparsity of the events. Specifically, we exploit the ability of correntropy to asses higher - order moments as well as imply the degree of similarity between two random variables in the joint space regulated by the kernel bandwidth. We test our algorithm on DREAMS Sleep Spindle Database and further elaborate on the hyperparameters introduced. Finally, we compare the performance of the algorithm with two algorithms designed on similar ideas; one of which is a quick, simple norm based technique while the other parallels the state-of-the-art Robust Principal Component Analysis (RPCA) to achieve classification. The algorithm is able to match the performance of the state-of-the-art techniques while saving tremendously on computation time and complexity.