A review of ways to measure brain cognitive load and machine learning methods for their identification from EEG data
https://doi.org/10.25881/18110193_2024_3_20
Abstract
Aim. The purpose of this article is review and analyze methods for measuring cognitive load, as well as approaches to using machine learning techniques to identify EEG data.
Materials and methods. The review systematizes and summarizes the information on the topic under consideration. Scientific articles were searched in bibliographic databases: eLIBRARY, ScienceDirect, Scopus.
Results. This review focused on ways to measure the cognitive load of the brain, modern EEG recording devices, and methods for transforming, extracting, and classifying features from acquired EEG signals.
Conclusion. With new wearable devices available for acquiring and processing EEG signals, there is a need to develop new approaches for using machine learning to identify cognitive brain processes.
About the Authors
A. E. Dedkov
RUDN
Russian Federation
Russian Federation
Moscow
D. A. Andrikov
RUDN
Russian Federation
Russian Federation
PhD, Associate Professor
Moscow
A. E. Hramov
Immanuel Kant Baltic Federal University
Russian Federation
Russian Federation
DSc., Professor
Kaliningrad
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Review
For citations:
Dedkov A.E., Andrikov D.A., Hramov A.E. A review of ways to measure brain cognitive load and machine learning methods for their identification from EEG data. Medical Doctor and Information Technologies. 2024;(3):20-31. (In Russ.) https://doi.org/10.25881/18110193_2024_3_20
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