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Neurophysiological Visual Classification Indicators in the Brain-Computer Interface

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Engineering Psychology and Cognitive Ergonomics (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12767))

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Abstract

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 images with 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.

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Authors and Affiliations

  1. St. Petersburg State Pediatric Medical University, St. Petersburg, 194100, Russia

    Sergey Lytaev

  2. St. Petersburg Federal Research Center of the RAS, St. Petersburg, 199178, Russia

    Sergey Lytaev

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  1. Sergey Lytaev
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Editors and Affiliations

  1. Coventry University, Coventry, UK

    Don Harris

  2. Cranfield University, Cranfield, UK

    Wen-Chin Li

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Lytaev, S. (2021). Neurophysiological Visual Classification Indicators in the Brain-Computer Interface. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. HCII 2021. Lecture Notes in Computer Science(), vol 12767. Springer, Cham. https://doi.org/10.1007/978-3-030-77932-0_17

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  • DOI: https://doi.org/10.1007/978-3-030-77932-0_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77931-3

  • Online ISBN: 978-3-030-77932-0

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