Neurofeedback training for enhancement of the focused attention related to athletic performance in elite rifle shooters
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 10830 LNCS, Page: 106-119
2018
- 7Citations
- 33Captures
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Conference Paper Description
NeuroFeedback Training (NFT) is a type of biofeedback training using Electroencephalogram (EEG) that allows the subjects to do self-regulation during the training according to their real-time brain activities. The purpose of this study is to optimize focused attention in expert rifle shooters with the use of NFT tools and to enhance shooting performance. We designed and implemented an experiment, conducted NFT sessions, and confirmed that NFT can boost performance of the shooters. The efficiency of the NFT was examined by comparing the shooters’ performance, their results of standardized tests of general cognitive abilities on the Vienna Test System (VTS), and the brain patterns in before and after NFT sessions. According to the results, we confirmed that NFT can be used to boost the shooters’ performance. EEG data were recorded during the shooting tasks. We extracted different types of EEG-based indexes and identified the emotion and mental workload levels of the shooters right before they pulled the trigger. These indexes and emotion/workload levels were then correlated with the shooting scores to understand what are the optimal brain states for “good” shots. According to the results, we confirmed that (1) mental workload level is negatively correlated with the shooting performance; (2) the correlations analyses results between EEG-based power features and shooting performance are consistent with the literature review results; (3) the difference of brain states in the before and after NFT shooting session could be because of NFT.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85043990154&origin=inward; http://dx.doi.org/10.1007/978-3-662-56672-5_8; http://link.springer.com/10.1007/978-3-662-56672-5_8; http://link.springer.com/content/pdf/10.1007/978-3-662-56672-5_8; https://dx.doi.org/10.1007/978-3-662-56672-5_8; https://link.springer.com/chapter/10.1007/978-3-662-56672-5_8
Springer Science and Business Media LLC
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