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

Research output: Contribution to journalReview articlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number107135
JournalComputers in Biology and Medicine
Volume163
DOIs
Publication statusPublished - Sept 2023

Keywords

  • Artificial intelligence
  • Artificial neural networks
  • Brain–computer interfaces
  • Fuzzy logic
  • Machine learning
  • Nature-inspired optimization techniques

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