EEG predicts the attention level of elderly measured by RBANS

International Journal of Crowd Science

Wooi Boon Goh^1¹  Tih-Shih Lee^2²  Fatemeh Fahimi^1³ 

[1] Duke-NUS Medical School , Singapore^2;Nanyang Technological University , Nanyang, Singapore ...Show all authors^3;Nanyang Technological University , Nanyang, Singapore^1

关键词: Elderly;    Spectral analysis;    EEG;    Attention;    Brain-computer interface;    RBANS;   

DOI  :  10.1108/IJCS-09-2018-0022

学科分类：工程和技术（综合）

来源: Emerald Publishing

【 摘 要 】

Purpose This study aims to investigate the correlation between neural indexes of attention and behavioral indexes of attention and detect the most informative period of brain activity in which the strongest correlation with attentive performance (behavioral index) exists. Finally, to further validate the findings, this paper aims at the prediction of different levels of attention function based on the attention score obtained from repeatable battery for the assessment of neurophysiological status (RBANS). Design/methodology/approach The present paper analyzes electroencephalogram (EEG) signals recorded by a single prefrontal channel from 105 elderly subjects while they were responding to Stroop color test which is an attention-demanded task. Beside Stroop test, subjects also performed RBANS which provides their level of functionality in different domains including attention. After data acquisition (EEG during Stroop test and RBANS attention score), the authors extract the spectral features of EEG as neural indexes of attention and subjects’ reaction time in response to Stroop test as behavioral index of attention. Then, they explore the correlation between these post-cue frequency band oscillations of EEG with elderly response time (RT). Next, the authors exploit these findings to classify RBANS attention score. Findings The observations of this study suggest that there is significant negative correlation between alpha gamma ratio (AGR) and RT (p pResearch limitations/implications Because of the experiment design, the pre-cue EEG of the next trail was very much overlapped with the post-cue EEG of the current trail. Therefore, the authors could analyze only post-cue EEG. In future study, it would be interesting to investigate the predictability of subject’s future performance from pre-cue EEG and mental preparation. Practical implications This study provides an insight into the research on detection of human attention level from EEG instead of conventional neurophysiological tests. It has also potential to be used in implementation of feasible and efficient EEG-based brain computer interface training systems for elderly. Originality/value To the best of the authors’ knowledge, this study is among very few attempts for early prediction of cognitive decline in the domain of attention from brain activity (EEG) instead of conventional tests which are prone to human errors.

【 授权许可】

CC BY