BCI-Walls: a robust methodology to predict if conscious EEG changes can be detected in the presence of artefacts

Porr, B. and Bohollo, L. M. (2023) BCI-Walls: a robust methodology to predict if conscious EEG changes can be detected in the presence of artefacts. PLoS ONE, 18(8), e0290446. (doi: 10.1371/journal.pone.0290446) (PMID:37616245) (PMCID:PMC10449140)

Abstract

Brain computer interfaces (BCI) depend on reliable realtime detection of conscious EEG changes for example to control a video game. However, scalp recordings are contaminated with non-stationary noise, such as facial muscle activity and eye movements. This interferes with the detection process making it potentially unreliable or even impossible. We have developed a new methodology which provides a hard and measurable criterion if conscious EEG changes can be detected in the presence of non-stationary noise by requiring the signal-to-noise ratio of a scalp recording to be greater than the SNR-wall which in turn is based on the highest and lowest noise variances of the recording. As an instructional example, we have recorded signals from the central electrode Cz during eight different activities causing non-stationary noise such as playing a video game or reading out loud. The results show that facial muscle activity and eye-movements have a strong impact on the detectability of EEG and that minimising both eye-movement artefacts and muscle noise is essential to be able to detect conscious EEG changes.

• Item Type: Articles
• Additional Information: This work was supported in part by the School of Engineering, University of Glasgow.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Porr, Dr Bernd and Munoz Bohollo, Miss Lucia

Creator Roles

Porr, B.: Conceptualization, Formal analysis, Methodology, Software, Supervision, Writing – original draft, Writing – review and editing
Bohollo, L. M.: Data curation, Formal analysis, Investigation, Software, Writing – original draft, Writing – review and editing

• Authors: Porr, B., and Bohollo, L. M.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Biomedical Engineering
• Journal Name: PLoS ONE
• Publisher: Public Library of Science
• ISSN: 1932-6203
• ISSN (Online): 1932-6203
• Copyright Holders: Copyright © 2023 Porr, Bohollo
• First Published: First published in PLoS ONE 18(8): e0290446
• Publisher Policy: Reproduced under a Creative Commons License

Related URLs

• Research Data

• Data DOI: 10.5525/gla.researchdata.1258