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

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

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, cilt.24, ss.2550-2558, 2020 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1109/jbhi.2020.2980056
  • Dergi Adı: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.2550-2558
  • Anahtar Kelimeler: 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
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Hacettepe Üniversitesi Adresli: Evet

Özet

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

ABSTRACT

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.