EEG datasets for healthcare: A scoping review

Peres da Silva, Caroline; Tedesco, Salvatore; O’Flynn, Brendan

EEG datasets for healthcare: A scoping review

cb

Files

EEG_Datasets_for_Healthcare_A_Scoping_Review.pdf(2.05 MB)

Published Version

Date

2024-03-11

Authors

Peres da Silva, Caroline

Tedesco, Salvatore

O’Flynn, Brendan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published Version

https://doi.org/10.1109/ACCESS.2024.3376254

Abstract

The rapidly evolving landscape of artificial intelligence (AI) and machine learning has placed data at the forefront of healthcare innovation. Electroencephalography (EEG) has gained significant attention for its potential to revolutionize healthcare applications. However, the effective utilization of EEG data in advancing medical diagnoses and treatment hinges on the availability and quality of relevant datasets. In this context, we conducted a scoping review to explore the wealth of EEG datasets designed for healthcare applications. This review serves as a critical exploration of the current landscape, aiming to identify datasets related to healthcare conditions while assessing their reusability. Our findings highlight both the opportunities and limitations in the wealth of open access EEG datasets. Available. As AI increasingly relies on high-quality, well labelled data, barriers impeding the sharing and utilization of EEG data for healthcare (such as lack of comprehensive documentation or adherence to FAIR principles) must be addressed so as to leverage the potential of advanced deep learning models to unlock new possibilities for diagnosis and analysis of a wide array of medical conditions.

Keywords

Electroencephalography , EEG , Deep learning , Open acc , Data sets

Citation

Peres da Silva, C., Tedesco, S. and O’Flynn, B. (2024) 'EEG datasets for healthcare: A scoping review', IEEE Access, 12, pp. 39186-39203. https://doi.org/10.1109/ACCESS.2024.3376254