Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Featuresopen access
- Authors
- Han, Chang-Hee; Lim, Jeong-Hwan; Lee, Jun-Hak; Kim, Kangsan; Im, Chang-Hwan
- Issue Date
- 2016
- Publisher
- HINDAWI LTD
- Citation
- BIOMED RESEARCH INTERNATIONAL, v.2016, pp.1 - 9
- Indexed
- SCIE
SCOPUS
- Journal Title
- BIOMED RESEARCH INTERNATIONAL
- Volume
- 2016
- Start Page
- 1
- End Page
- 9
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/24743
- DOI
- 10.1155/2016/3939815
- ISSN
- 2314-6133
- Abstract
- It has frequently been reported that some users of conventional neurofeedback systems can experience only a small portion of the total feedback range due to the large interindividual variability of EEG features. In this study, we proposed a data-driven neurofeedback strategy considering the individual variability of electroencephalography (EEG) features to permit users of the neurofeedback system to experience a wider range of auditory or visual feedback without a customization process. The main idea of the proposed strategy is to adjust the ranges of each feedback level using the density in the offline EEG database acquired from a group of individuals. Twenty-two healthy subjects participated in offline experiments to construct an EEG database, and five subjects participated in online experiments to validate the performance of the proposed data-driven user feedback strategy. Using the optimized bin sizes, the number of feedback levels that each individual experienced was significantly increased to 139% and 144% of the original results with uniform bin sizes in the offline and online experiments, respectively. Our results demonstrated that the use of our data-driven neurofeedback strategy could effectively increase the overall range of feedback levels that each individual experienced during neurofeedback training.
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