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
  • [21] Automatic Sleep Staging Based on Contextual Scalograms and Attention Convolution Neural Network Using Single-Channel EEG
    Wei, Yu
    Zhu, Yongpeng
    Zhou, Yihan
    Yu, Xiaokang
    Luo, Yuxi
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2024, 28 (02) : 801 - 811
  • [22] Continuous EEG Decoding of Pilots' Mental States Using Multiple Feature Block-Based Convolutional Neural Network
    Lee, Dae-Hyeok
    Jeong, Ji-Hoon
    Kim, Kiduk
    Yu, Baek-Woon
    Lee, Seong-Whan
    IEEE ACCESS, 2020, 8 (08): : 121929 - 121941
  • [23] Investigating the Brain Development in Newborns by Information-Based Analysis of Electroencephalography (EEG) Signal
    Namazi, Hamidreza
    FLUCTUATION AND NOISE LETTERS, 2020, 19 (04):
  • [24] Wavelet-Neural Classification of Sleep EEG under Stressful Condition
    Upadhyay, P. K.
    Sinha, R. K.
    2013 SIXTH INTERNATIONAL CONFERENCE ON DEVELOPMENTS IN ESYSTEMS ENGINEERING (DESE), 2014, : 93 - 98
  • [25] A Survey of EEG and Machine Learning-Based Methods for Neural Rehabilitation
    Singh, Jaiteg
    Ali, Farman
    Gill, Rupali
    Shah, Babar
    Kwak, Daehan
    IEEE ACCESS, 2023, 11 : 114155 - 114171
  • [26] Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals
    Dumitrescu, Catalin
    Costea, Ilona-Madalina
    Cormos, Angel-Ciprian
    Semenescu, Augustin
    SENSORS, 2021, 21 (21)
  • [27] Brain Tumor Detection using Scalp EEG with Modified Wavelet-ICA and Multi Layer Feed Forward Neural Network
    Selvam, V. Salai
    Shenbagadevi, S.
    2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2011, : 6104 - 6109
  • [28] EEG-to-EEG: Scalp-to-Intracranial EEG Translation Using a Combination of Variational Autoencoder and Generative Adversarial Networks
    Abdi-Sargezeh, Bahman
    Shirani, Sepehr
    Valentin, Antonio
    Alarcon, Gonzalo
    Sanei, Saeid
    SENSORS, 2025, 25 (02)
  • [29] EEG signature of near-death-like experiences during syncope-induced periods of unresponsiveness
    Martial, Charlotte
    Piarulli, Andrea
    Gosseries, Olivia
    Cassol, Helena
    Ledoux, Didier
    Charland-Verville, Vanessa
    Laureys, Steven
    NEUROIMAGE, 2024, 298
  • [30] EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification
    Zhang, Ce
    Kim, Young-Keun
    Eskandarian, Azim
    JOURNAL OF NEURAL ENGINEERING, 2021, 18 (04)