Prediction of Reaction Time and Vigilance Variability From Spatio-Spectral Features of Resting-State EEG in a Long Sustained Attention Task


Torkamani-Azar M., Kanik S. D. , Aydın S., Cetin M.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, vol.24, pp.2550-2558, 2020 (Peer-Reviewed Journal) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 24
  • Publication Date: 2020
  • Doi Number: 10.1109/jbhi.2020.2980056
  • Journal Name: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
  • Journal Indexes: Science Citation Index Expanded
  • Page Numbers: pp.2550-2558
  • Keywords: Task analysis, Electroencephalography, Informatics, Time factors, Monitoring, Brain modeling, Electrodes, Brain-computer interface, resting-state analysis, electroencephalography, neural networks, multivariate regression, human performance, sustained attention, vigilance, default mode network, BRAIN-COMPUTER INTERFACE, COGNITIVE PERFORMANCE, ACTIVATION, PATTERNS, MODEL, POWER

Abstract

https://ieeexplore.ieee.org/document/9034192

Resting-state brain networks represent the intrinsic state of the brain during the majority of cognitive and sensorimotor tasks. However, no study has yet presented concise predictors of task-induced vigilance variability from spectro-spatial features of the resting-state electroencephalograms (EEG). In this study, ten healthy volunteers have participated in fixed-sequence, varying-duration sessions of sustained attention to response task (SART) for over 100 minutes. A novel and adaptive cumulative vigilance scoring (CVS) scheme is proposed based on tonic performance and response time. Multiple linear regression (MLR) using feature relevance analysis has shown that average CVS, average response time, and variabilities of these scores can be predicted (p < 0.05) from the resting-state band-power ratios of EEG signals. Cross-validated neural networks also captured different associations for narrow-band beta and wide-band gamma and differences between the high- and low-attention networks in temporal regions. The proposed framework and these first findings on stable and significant attention predictors from the power ratios of resting-state EEG can be useful in brain-computer interfacing and vigilance monitoring applications.