Extending statistical data of EEG biofeedback quality improvement with soft computing


  • Łukasz Czuba Department of Naval Command and Operations, Polish Naval Academy of the Heroes of Westerplatte, Inżyniera Jana Śmidowicza 69, 81-127 Gdynia, Poland https://orcid.org/0009-0009-8601-975X
  • Monika Łukowska Department of Methodology, Statistics and Informatics, Faculty of Theory and Practice of Sport, The Jerzy Kukuczka Academy of Physical Education, ul. Mikołowska 72a 40-065 Katowice, Poland https://orcid.org/0009-0000-2759-1654




Aim. Automatic processing of the data in order to determine the status of work and identification of the activity and brain-wave frequencies becomes necessary for the modern systems in the in the diagnosis of biofeedback among athletes.

Concept. The study aimed to explore the effects of physical exertion on alterations in the manifestation of brain wave frequencies (pre/post exercises) in a group of 15 endurance athletes.

Results and conclusion. Statistic methods allowed an identification of data anomalies, such as extreme, outliers and missing values. Combining information with soft computing tool can distinguish the level of electrical activity of the analysed muscles. Used Big Data and Data Mining tools solution with a statistical approach while maintaining high measurement accuracy indicates the effectiveness of this method in medical diagnosis.


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How to Cite

Czuba, Łukasz, & Łukowska, M. (2023). Extending statistical data of EEG biofeedback quality improvement with soft computing. E-Methodology, 9(9), 40–49. https://doi.org/10.15503/emet.2022.40.49



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