Neurophysiological Visual Classification Indicators in the Brain-Computer Interface

被引:0
|
作者
Lytaev, Sergey [1 ,2 ]
机构
[1] St Petersburg State Pediat Med Univ, St Petersburg 194100, Russia
[2] RAS, St Petersburg Fed Res Ctr, St Petersburg 199178, Russia
关键词
Event-related potentials; Visual evoked potentials; Wave P-300; Brain-computer interface; Oddball paradigm; Categorization of images; COGNITIVE NEUROSCIENCE; INSIGHT; PEOPLE;
D O I
10.1007/978-3-030-77932-0_17
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The article presents the results of original research in the context of discussion of modern studies of the well-known psychological phenomenon of P300 evoked potentials in Brain Computer Interaction (BCI) systems. The aim of this research was to study the invariant processes of perception of the model "human-computer interaction" when classifying visual imageswith an incomplete set of features based on the analysis of the early, middle, late and slow components (up to 1000 ms) of event-related potentials (ERP). 26 healthy subjects (men) aged 20-26 years were investigated. Visual evoked potentials (VEPs) in 19 monopolar sites from the head surface according to the 10/20 system were recorded. The stimuli were a number of visual images with an incomplete set of features used in neuropsychological research. ERPs were analyzed at a time interval of 1000.0 ms from the moment of stimulation, using data from topographic brain mapping, as well as an assessment of the spatiotemporal characteristics of ERPs. Stepwise discriminant and factor analysis to establish the stability of ERPs parameters were applied. The results made it possible to establish that component N450 is the most specialized indicator of the perception of unrecognizable (oddball) visual images. The amplitude of the ultra-late components N750 and N900 is also higher under conditions of presentation of the oddball image, regardless of the location of the registration points.
引用
收藏
页码:197 / 211
页数:15
相关论文
共 50 条
  • [41] EarEEG based Visual P300 Brain-Computer Interface
    Farooq, Faisal
    Looney, David
    Mandic, Danilo P.
    Kidmose, Preben
    2015 7TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), 2015, : 98 - 101
  • [42] 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
  • [43] Feature Extraction and Classification in A Two-State Brain-Computer Interface
    Altindis, Fatih
    Yilmaz, Bulent
    2016 MEDICAL TECHNOLOGIES NATIONAL CONFERENCE (TIPTEKNO), 2015,
  • [44] 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,
  • [45] Classification of ECoG with modified S-transform for brain-computer interface
    Xu, Fangzhou
    Zhou, Weidong
    Zhen, Yilin
    Yuan, Qi
    Journal of Computational Information Systems, 2014, 10 (18): : 8029 - 8041
  • [46] A Novel Classification Method for Motor Imagery Based on Brain-Computer Interface
    Chen, Chih-Yu
    Wu, Chun-Wei
    Lin, Chin-Teng
    Chen, Shi-An
    PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2014, : 4099 - 4102
  • [47] Classification of motor imagery tasks for electrocorticogram based brain-computer interface
    Xu F.
    Zhou W.
    Zhen Y.
    Yuan Q.
    Zhou, W. (wdzhou@sdu.edu.cn), 1600, Springer Verlag (04): : 149 - 157
  • [48] WAVELET SHRINKAGE AND THRESHOLDING BASED ROBUST CLASSIFICATION FOR BRAIN-COMPUTER INTERFACE
    Banerjee, Taposh
    Choi, John
    Pesaran, Bijan
    Ba, Demba
    Tarokh, Vahid
    2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2018, : 836 - 840
  • [49] Electroencephalography-Based Brain-Computer Interface Motor Imagery Classification
    Mohammadi, Ehsan
    Daneshmand, Parisa Ghaderi
    Khorzooghi, Seyyed Mohammad Sadegh Moosavi
    JOURNAL OF MEDICAL SIGNALS & SENSORS, 2022, 12 (01): : 40 - 47
  • [50] Feature extraction and parameters selection of classification model on brain-computer interface
    Zhao, Mingyuan
    Zhou, Mingtian
    Zhu, Qingxin
    Yang, Ping
    PROCEEDINGS OF THE 7TH IEEE INTERNATIONAL SYMPOSIUM ON BIOINFORMATICS AND BIOENGINEERING, VOLS I AND II, 2007, : 1249 - +