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Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Title
Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications
Author
임창환
Keywords
electroencephalography (EEG); interindividual variability; resting state EEG; passive brain-computer interfaces; machine learning
Issue Date
2020-03
Publisher
MDPI
Citation
SENSORS, v. 20, no. 4, article no. 988
Abstract
With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain-computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user's dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.
URI
https://www.mdpi.com/1424-8220/20/4/988https://repository.hanyang.ac.kr/handle/20.500.11754/161969
ISSN
1424-8220
DOI
10.3390/s20040988
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > ELECTRICAL AND BIOMEDICAL ENGINEERING(전기·생체공학부) > Articles
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