Investigating the neural signature of microsleeps using EEG

被引:0
|
作者
Zaky, Mohamed H. [1 ,2 ,3 ,4 ]
Shoorangiz, Reza [4 ,5 ,6 ]
Poudel, Govinda R. [3 ]
Yang, Le [1 ]
Jones, Richard D. [4 ,7 ,8 ]
机构
[1] Univ Canterbury, Dept Elect & Comp Engn, Christchurch, New Zealand
[2] Arab Acad Sci Technol & Maritime Transport Egypt, Giza, Egypt
[3] Christchurch Neurotechnol Res Programme, Christchurch, New Zealand
[4] New Zealand Brain Res Inst, Christchurch, New Zealand
[5] Univ Canterbury, Dept Elect & Comp Engn, Christchurch Neurotechnol Res Programme, Christchurch, New Zealand
[6] Australian Catholic Univ, Sydney, NSW, Australia
[7] Univ Canterbury, Dept Elect & Comp Engn & Psychol, Christchurch Neurotechnol Res Programme, Canterbury, New Zealand
[8] Univ Otago, Dept Med, Dunedin, New Zealand
来源
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) | 2021年
关键词
CORTICAL ACTIVITY; SLEEP; ARTIFACTS; LAPSES;
D O I
10.1109/EMBC46164.2021.9630401
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
A microsleep (MS) is a complete lapse of responsiveness due to an episode of brief sleep (less than or similar to 15 s) with eyes partially or completely closed. MSs are highly correlated with the risk of car accidents, severe injuries, and death. To investigate EEG changes during MSs, we used a 2D continuous visuomotor tracking (CVT) task and eye-video to identify MSs in 20 subjects performing the 50-min task. Following pre-processing, FFT spectral analysis was used to calculate the activity in the EEG delta, theta, alpha, beta, and gamma bands, followed by eLORETA for source reconstruction. A group statistical analysis was performed to compare the change in activity over EEG bands of an MS to its baseline. After correction for multiple comparisons, we found maximum increases in delta, theta, and alpha activities over the frontal lobe, and beta over the parietal and occipital lobes. There were no significant changes in the gamma band, and no significant decreases in any band. Our results are in agreement with previous studies which reported increased alpha activity in MSs. However, this is the first study to have reported increased beta activity during MSs, which, due to the usual association of beta activity with wakefulness, was unexpected.
引用
收藏
页码:6293 / 6296
页数:4
相关论文
共 50 条
  • [1] Detection and Prediction of Microsleeps from EEG using Spatio-Temporal Patterns
    Shoorangiz, Reza
    Buriro, Abdul Baseer
    Weddell, Stephen J.
    Jones, Richard D.
    2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2019, : 522 - 525
  • [2] Prediction of Microsleeps from EEG: Preliminary Results
    Shoorangiz, Reza
    Weddell, Stephen J.
    Jones, Richard D.
    2016 38TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2016, : 4650 - 4653
  • [3] Time-Varying Functional Connectivity for Understanding the Neural Basis of Behavioral Microsleeps
    Toppi, J.
    Astolfi, L.
    Poudel, G. R.
    Babiloni, F.
    Macchiusi, L.
    Mattia, D.
    Salinari, S.
    Jones, R. D.
    2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2012, : 4708 - 4711
  • [4] Voluntary Respiration Control: Signature Analysis by EEG
    Wang, Yue
    Zhang, Yan
    Zhang, Yaoxi
    Wang, Zongyu
    Guo, Weidong
    Zhang, Yuru
    Wang, Yuhui
    Ge, Qinggang
    Wang, Dangxiao
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2023, 31 : 4624 - 4634
  • [5] Investigating Window Segmentation on Mental Fatigue Detection Using Single-channel EEG
    Hendrawani, Muhammad Afif
    Pane, Evi Septiana
    Wibawa, Adhi Dharma
    Purnomo, Mauridhi Fiery
    PROCEEDINGS OF 2017 5TH INTERNATIONAL CONFERENCE ON INSTRUMENTATION, COMMUNICATIONS, INFORMATION TECHNOLOGY, AND BIOMEDICAL ENGINEERING (ICICI-BME): SCIENCE AND TECHNOLOGY FOR A BETTER LIFE, 2017, : 173 - 178
  • [6] Active Learning Approach for EEG Classification using Neural Networks: A review
    Sebek, Jakub
    Schaabova, Hana
    Krajca, Vladimir
    2019 E-HEALTH AND BIOENGINEERING CONFERENCE (EHB), 2019,
  • [7] Dynamic neural reconstructions of attended object location and features using EEG
    Chen, Jiageng
    Golomb, Julie D. D.
    JOURNAL OF NEUROPHYSIOLOGY, 2023, 130 (01) : 139 - 154
  • [8] Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients
    Subasi, A
    EXPERT SYSTEMS WITH APPLICATIONS, 2005, 28 (04) : 701 - 711
  • [9] Investigating Nuisance Effects Induced in EEG During tACS Application
    Holzmann, Romain
    Koppehele-Gossel, Judith
    Voss, Ursula
    Klimke, Ansgar
    FRONTIERS IN HUMAN NEUROSCIENCE, 2021, 15
  • [10] An EEG Signature of MCH Neuron Activities Predicts Cocaine Seeking in Rats
    Wang, Yao
    Li, Danyang
    Widjaja, Joseph
    Guo, Rong
    Cai, Li
    Yan, Rongzhen
    Ozsoy, Sahin
    Allocca, Giancarlo
    Fang, Jidong
    Dong, Yan
    Tseng, George
    Huang, Chengcheng
    Huang, Yanhua
    NEUROPSYCHOPHARMACOLOGY, 2023, 48 : 415 - 416