Brain-Computer Interface on the Basis of EEG System "Encephalan"

被引:6
|
作者
Maksimenko, Vladimir [1 ]
Badarin, Artem [1 ]
Nedaivozov, Vladimir [1 ]
Kirsanov, Daniil [1 ]
Hramov, Alexander [1 ]
机构
[1] Yurij Gagarin State Tech Univ Saratov, REC Artificial Intelligence Syst & Neurotechnol, Politech Skaya Str 77, Saratov 410056, Russia
关键词
Electroencephalogram; continuous wavelet analysis; brain-computer interface; concentration of attention; PERCEPTION;
D O I
10.1117/12.2314651
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
We have proposed brain-computer interface (BCI) for the estimation of the brain response on the presented visual tasks. Proposed BCI is based on the EEG recorder Encephalan-EEGR-19/26 (Medicom MTD, Russia) supplemented by a special home-made developed acquisition software. BCI is tested during experimental session while subject is perceiving the bistable visual stimuli and classifying them according to the interpretation. We have subjected the participant to the different external conditions and observed the significant decrease in the response, associated with the perceiving the bistable visual stimuli, during the presence of distraction. Based on the obtained results we have proposed possibility to use of BCI for estimation of the human alertness during solving the tasks required substantial visual attention.
引用
收藏
页数:6
相关论文
共 50 条
  • [41] Discriminative Dictionary Learning for EEG Signal Classification in Brain-Computer Interface
    Zhou, Wei
    Yang, Ya
    Yu, Zhuliang
    2012 12TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS & VISION (ICARCV), 2012, : 1582 - 1585
  • [42] EEG-Based Brain-Computer Interface for Control of Assistive Devices
    Kapralov, Nikolay, V
    Ekimovskii, Jaroslav, V
    Potekhin, Vyacheslav V.
    CYBER-PHYSICAL SYSTEMS AND CONTROL, 2020, 95 : 536 - 543
  • [43] An HMM-based Eye Movement Detection System Using EEG Brain-Computer Interface
    Hsieh, Chi-Hsuan
    Chu, Hao-Ping
    Huang, Yuan-Hao
    2014 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), 2014, : 662 - 665
  • [44] A regularized discriminative framework for EEG analysis with application to brain-computer interface
    Tomioka, Ryota
    Mueller, Klaus-Robert
    NEUROIMAGE, 2010, 49 (01) : 415 - 432
  • [45] Brain-Computer Interface with Corrupted EEG Data: a Tensor Completion Approach
    J. Solé-Casals
    C. F. Caiafa
    Q. Zhao
    A. Cichocki
    Cognitive Computation, 2018, 10 : 1062 - 1074
  • [46] Classification of EEG Signals for Brain-Computer Interface Applications: Performance Comparison
    Ilyas, M. Z.
    Saad, P.
    Ahmad, M. I.
    Ghani, A. R. I.
    PROCEEDINGS OF 2016 INTERNATIONAL CONFERENCE ON ROBOTICS, AUTOMATION AND SCIENCES (ICORAS 2016), 2016,
  • [47] A Brain-Computer Interface Based on a Few-Channel EEG-fNIRS Bimodal System
    Ge, Sheng
    Yang, Qing
    Wang, Ruimin
    Lin, Pan
    Gao, Junfeng
    Leng, Yue
    Yang, Yuankui
    Wang, Haixian
    IEEE ACCESS, 2017, 5 : 208 - 218
  • [48] EEG denoising with a Recurrent Quantum Neural Network for a Brain-Computer Interface
    Gandhi, Vaibhav
    Arora, Vipul
    Behera, Laxmidhar
    Prasad, Girijesh
    Coyle, Damien
    McGinnity, T. M.
    2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2011, : 1583 - 1590
  • [49] An EEG-based Brain-Computer Interface for Attention State Recognition
    Tang, Yongchao
    Huang, Haiyun
    2020 INTERNATIONAL SYMPOSIUM ON AUTONOMOUS SYSTEMS (ISAS), 2020, : 100 - 104
  • [50] Prosthetic control by an EEG-based brain-computer interface (BCI)
    Guger, C
    Harkam, W
    Hertnaes, C
    Pfurtscheller, G
    ASSISTIVE TECHNOLOGY ON THE THRESHOLD OF THE NEW MILLENNIUM, 1999, 6 : 590 - 595