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A Brain Computer Interface Based Visual Keyboard System Using SSVEP and Electrooculogram

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Machine Learning and Metaheuristics Algorithms, and Applications (SoMMA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1203))

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Abstract

This study aims to design a steady-state visual evoked potential (SSVEP) based, on-screen keyboard/speller system along with the integration of electrooculogram (EOG). The characters/targets were designed using the pattern reversal square checkerboard flickering visual stimuli. In this study, twenty-three characters were randomly selected and their corresponding visual stimuli were designed using five frequencies (6, 6.667, 7.5, 8.57 and 10 Hz). The keyboard layout was divided into nine regions and each region was identified by using the subject’s eye gaze information with the help of EOG data. The information from the EOG was used to locate the area on the visual keyboard/display, where the subject is looking. The region identification helps to use the same frequency valued visual stimuli more than once on the keyboard layout. In this proposed study, more targets were designed using less number of visual stimulus frequencies by integrating EOG with the SSVEP keyboard system. The multi-threshold algorithm and extended multivariate synchronization index (EMSI) method were used for eye gaze detection and SSVEP frequency recognition respectively. Ten healthy subjects were recruited for validating the proposed visual keyboard system.

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Author information

Authors and Affiliations

  1. Department of Applied Mechanics, Biomedical Group, Indian Institute of Technology Madras, Chennai, India

    D. Saravanakumar & Machireddy Ramasubba Reddy

Authors
  1. D. Saravanakumar
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  2. Machireddy Ramasubba Reddy
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Corresponding author

Correspondence to D. Saravanakumar .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology and Management - Kerala (IIITM-K), Trivandrum, India

    Sabu M. Thampi

  2. Simon Fraser University, Burnaby, BC, Canada

    Ljiljana Trajkovic

  3. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  4. Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  5. Wrocław University of Technology, Wrocław, Poland

    Michal Wozniak

  6. Università degli Studi di Firenze, Florence, Italy

    Stefano Berretti

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Saravanakumar, D., Ramasubba Reddy, M. (2020). A Brain Computer Interface Based Visual Keyboard System Using SSVEP and Electrooculogram. In: Thampi, S., Trajkovic, L., Li, KC., Das, S., Wozniak, M., Berretti, S. (eds) Machine Learning and Metaheuristics Algorithms, and Applications. SoMMA 2019. Communications in Computer and Information Science, vol 1203. Springer, Singapore. https://doi.org/10.1007/978-981-15-4301-2_6

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  • DOI: https://doi.org/10.1007/978-981-15-4301-2_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4300-5

  • Online ISBN: 978-981-15-4301-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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