Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction

Katerina Barnova; Martina Mikolasova; Radana Vilimkova Kahankova; Rene Jaros; Aleksandra Kawala-Sterniuk; Vaclav Snasel; Seyedali Mirjalili; Mariusz Pelc; Radek Martinek

doi:10.1016/j.compbiomed.2023.107135

Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction

Comput Biol Med. 2023 Sep:163:107135. doi: 10.1016/j.compbiomed.2023.107135. Epub 2023 Jun 8.

Authors

Katerina Barnova¹, Martina Mikolasova², Radana Vilimkova Kahankova³, Rene Jaros⁴, Aleksandra Kawala-Sterniuk⁵, Vaclav Snasel⁶, Seyedali Mirjalili⁷, Mariusz Pelc⁸, Radek Martinek⁹

Affiliations

¹ Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia. Electronic address: katerina.barnova@vsb.cz.
² Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia. Electronic address: martina.mikolasova@vsb.cz.
³ Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
⁴ Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia. Electronic address: rene.jaros@vsb.cz.
⁵ Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland. Electronic address: kawala84@gmail.com.
⁶ Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia. Electronic address: vaclav.snasel@vsb.cz.
⁷ Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Australia. Electronic address: ali.mirjalili@gmail.com.
⁸ Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland; School of Computing and Mathematical Sciences, University of Greenwich, London, UK. Electronic address: m.pelc@greenwich.ac.uk.
⁹ Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia; Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland. Electronic address: radek.martinek@vsb.cz.

PMID: 37329623
DOI: 10.1016/j.compbiomed.2023.107135

Abstract

Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.

Keywords: Artificial intelligence; Artificial neural networks; Brain–computer interfaces; Fuzzy logic; Machine learning; Nature-inspired optimization techniques.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Artificial Intelligence*
Brain
Brain-Computer Interfaces*
Computers
Humans
Machine Learning
Quality of Life