Month: July 2023

FAAI at European Games Esports Championships

FAAI at European Games Esports Championships

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The European Games Esports Championships (#EGE23) was accompanied by three prestigious substantive events, including an organized cyclical conference devoted to gaming and related industries in Central and Eastern Europe. Professionals, scientists and administrators shared their knowledge and experience in one place. The UBB team representative was taking part in a panel considered Gaming in education. He stressed the important part of AI in this this area. Additionally, the leaflets and poster were presented.

Panayotova, Galina S,Dimitrov, Georgi P,Dimitrov, Willian A,Petrov, Pavel S,Petrova, Pepa V,Tsvetkova, Paulina T, One Approach to using R for Bayesian Analysis of Brain Signals, 2023, 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2023 – Proceedings Pages 115 – 118 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2023Nis; 29 June 2023 through 1 July 2023; Code 191047 ; ISBN – 979-835031073-3; DOI – 10.1109/ICEST58410.2023.10187310; Publisher – Institute of Electrical and Electronics Engineers Inc.;

Panayotova, Galina S,Dimitrov, Georgi P,Dimitrov, Willian A,Petrov, Pavel S,Petrova, Pepa V,Tsvetkova, Paulina T, One Approach to using R for Bayesian Analysis of Brain Signals, 2023, 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2023 – Proceedings Pages 115 – 118 2023 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2023Nis; 29 June 2023 through 1 July 2023; Code 191047 ; ISBN – 979-835031073-3; DOI – 10.1109/ICEST58410.2023.10187310; Publisher – Institute of Electrical and Electronics Engineers Inc.;

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The study of brain signals based on efficient computing is a new beginning in the research field aimed at finding a connection between human emotions and recorded BCI signals. Analysis of incoming brain signals and techniques for processing and classifying information are actively researched. Investigating these signals is a complex process that requires multidisciplinary skills and knowledge in computer science, signal processing, neuroscience, robotics, artificial intelligence, and more. This research focuses on the classification of brain-computer interface (BCI) data using Bayesian analysis. The aim of the paper is to study the classification of brain signals and the possibilities of reducing the number of channels. Experimental data were obtained using Emotiv Epoc 14+. The R programming language was used to process the data. The data were classified using Bayesian analysis in R.