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

• Published in: Computers in biology and medicine Vol. 163; p. 107135
• Main Authors: Barnova, Katerina, Mikolasova, Martina, Kahankova, Radana Vilimkova, Jaros, Rene, Kawala-Sterniuk, Aleksandra, Snasel, Vaclav, Mirjalili, Seyedali, Pelc, Mariusz, Martinek, Radek
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2023; Elsevier Limited
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Biology; Brain research; Brain–computer interfaces; Communication; Computer engineering; Computer science; Computers; Electrical engineering; Electroencephalography; Fuzzy logic; Human-computer interface; Interfaces; Internal Medicine; Learning algorithms; Machine learning; Mental task performance; Nature-inspired optimization techniques; Neural networks; Noise reduction; Optimization techniques; Other; Quality of life; Fuzzy logic; Nature-inspired optimization techniques; Brain–computer interfaces; Artificial intelligence; Artificial neural networks; Machine learning; Brain-computer interfaces
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.107135

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. •A thorough review of AI- and ML-based methods in brain-computer interfaces.•Comprehensive description of invasive and non-invasive types of brain–computer interfaces.•Summary of AI- and ML-based solutions for brain signal processing and analysis.•Comparison of algorithms, key findings, and future research recommendations for brain-computer interface systems.