# Surrogate-based artifact removal from single-channel EEG

## TL;DR

This paper introduces SuBAR, a novel surrogate-based algorithm that effectively removes ocular and muscular artifacts from single-channel EEG signals, outperforming traditional methods and enabling reliable analysis in mobile health and brain-computer interface applications.

## Contribution

The paper presents a new data-driven surrogate-based method for artifact removal in single-channel EEG, improving noise reduction and reducing signal distortion compared to existing techniques.

## Key findings

- SuBAR achieves 4-5 times lower error than traditional methods.
- Effective in both mild and severe artifact conditions.
- Suitable for mobile health and brain-computer interface systems.

## Abstract

The recent emergence and success of electroencephalography (EEG) in low-cost portable devices, has opened the door to a new generation of applications processing a small number of EEG channels for health monitoring and brain-computer interfacing. These recordings are, however, contaminated by many sources of noise degrading the signals of interest, thus compromising the interpretation of the underlying brain state. In this work, we propose a new data-driven algorithm to effectively remove ocular and muscular artifacts from single-channel EEG: the surrogate-based artifact removal (SuBAR). Methods: By means of the time-frequency analysis of surrogate data, our approach is able to identify and filter automatically ocular and muscular artifacts embedded in single-channel EEG. Results: In a comparative study using artificially contami- nated EEG signals, the efficacy of the algorithm in terms of noise removal and signal distortion was superior to other traditionally-employed single-channel EEG denoising techniques: wavelet thresholding and the canonical correlation analysis combined with an advanced version of the empirical mode decomposition. Even in the presence of mild and severe artifacts, our artifact removal method provides a relative error 4 to 5 times lower than traditional techniques. Significance: In view of these results, the SuBAR method is a promising solution for mobile environments, such as ambulatory healthcare systems, sleep stage scoring or anesthesia monitoring, where very few EEG channels or even a single channel is available.