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Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features
Joint Authors
Han, Chang-Hee
Lim, Jeong-Hwan
Lee, Jun-Hak
Kim, Kangsan
Im, Chang-Hwan
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-18
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Han, Chang-Hee& Lim, Jeong-Hwan& Lee, Jun-Hak& Kim, Kangsan& Im, Chang-Hwan. 2016. Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features. BioMed Research International،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1097498
Modern Language Association (MLA)
Han, Chang-Hee…[et al.]. Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features. BioMed Research International No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1097498
American Medical Association (AMA)
Han, Chang-Hee& Lim, Jeong-Hwan& Lee, Jun-Hak& Kim, Kangsan& Im, Chang-Hwan. Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1097498
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1097498