Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features.
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 individu...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 8 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/18/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=117520404&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117520404 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/18/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 117520404 117520404 117520404 10.1155/2016/3939815 117520404 ppf: 1 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features. aug: au: Han, Chang-Hee Lim, Jeong-Hwan Lee, Jun-Hak Kim, Kangsan Im, Chang-Hwan affil: Department of Biomedical Engineering, Hanyang University, Seoul 133-731, Republic of Korea sug: subj: Electroencephalography Biofeedback Methods Measurement Issues and Assessments Human Resource Databases Descriptive Statistics Mental Disorders Therapy Sensory Stimulation Male Female Young Adult Adult Pilot Studies Algorithms Data Analysis Software Paired T-Tests P-Value Funding Source Adult: 19-44 years Male Female ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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