title: Data-Driven User Feedback: An Improved Neurofeedback Strategy considering the Interindividual Variability of EEG Features.
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.
authors.affiliation: Department of Biomedical Engineering, Hanyang University, Seoul 133-731, Republic of Korea.
authors.forename: Chang-Hee
authors.identifier: 0000-0002-9186-3295
authors.initials: CH
authors.lastname: Han
authors.affiliation: Department of Biomedical Engineering, Hanyang University, Seoul 133-731, Republic of Korea.
authors.forename: Jeong-Hwan
authors.initials: JH
authors.lastname: Lim
authors.affiliation: Department of Biomedical Engineering, Hanyang University, Seoul 133-731, Republic of Korea.
authors.forename: Jun-Hak
authors.initials: JH
authors.lastname: Lee
authors.affiliation: Department of Biomedical Engineering, Hanyang University, Seoul 133-731, Republic of Korea.
authors.forename: Kangsan
authors.initials: K
authors.lastname: Kim
authors.affiliation: Department of Biomedical Engineering, Hanyang University, Seoul 133-731, Republic of Korea.
authors.forename: Chang-Hwan
authors.identifier: 0000-0003-3795-3318
authors.initials: CH
authors.lastname: Im
mesh_terms: D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D056228:Feedback, Sensory / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D058765:Neurofeedback / Q000379:methods* / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012064:Relaxation Therapy / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055815:Young Adult