TY - JOUR
T1 - Implementation of artificial intelligence and machine learning-based methods in brain–computer interaction
AU - Barnova, Katerina
AU - Mikolasova, Martina
AU - Kahankova, Radana Vilimkova
AU - Jaros, Rene
AU - Kawala-Sterniuk, Aleksandra
AU - Snasel, Vaclav
AU - Mirjalili, Seyedali
AU - Pelc, Mariusz
AU - Martinek, Radek
N1 - Funding Information:
This work was supported by the European Regional Development Fund in the Research Centre of Advanced Mechatronic Systems project, project number CZ.02.1.01/0.0/0.0/16_019/0000867 within the Operational Programme Research, Development and Education. This article was supported by the Ministry of Education of the Czechia (Project No. SP2023/042). It also obtained partial funding from the Horizon 2020 FET program of the European Union through the ERA-NET Cofund funding grant CHIST-ERA-20-BCI-001 ( National Science Centre, Poland , under Grant Agreement no. .) The funders had no role in study design, data analysis, or results interpretation.
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/9
Y1 - 2023/9
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Artificial neural networks
KW - Brain–computer interfaces
KW - Fuzzy logic
KW - Machine learning
KW - Nature-inspired optimization techniques
UR - http://www.scopus.com/inward/record.url?scp=85162247167&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2023.107135
DO - 10.1016/j.compbiomed.2023.107135
M3 - Review article
C2 - 37329623
AN - SCOPUS:85162247167
SN - 0010-4825
VL - 163
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 107135
ER -