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Analyzing Image Classification via EEG

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Computer Vision, Pattern Recognition, Image Processing, and Graphics (NCVPRIPG 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1249))

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Abstract

Electroencephalogram (EEG) has been a popular technique for brain-computer interface (BCI) and brain decoding studies. However, decoding perceptual information (e.g. images and sound) is only recently being considered. In this work, we make an attempt to experimentally analyze and address the task of classification of images that a person sees, based on the captured EEG during the visual task. For this work, we used a publicly available dataset and, as a part of our analysis, find some important concerns associated with that dataset, which also highlights challenges in EEG based image classification. After we process the data to address these concerns, we show that there may still be some discriminative traits in the EEG data for classifying a small number of classes.

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Author information

Authors and Affiliations

  1. Multimedia Analytics, Networks and Systems Lab, School of Computing and Electrical Engineering, IIT Mandi, Mandi, India

    Rahul Mishra & Arnav Bhavsar

Authors
  1. Rahul Mishra
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  2. Arnav Bhavsar
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Corresponding author

Correspondence to Rahul Mishra .

Editor information

Editors and Affiliations

  1. Department of Computational and Data Sciences, Indian Institute of Science Bangalore, Bangalore, India

    R. Venkatesh Babu

  2. Department of Electrical Engineering, Indian Institute of Technology Dharwad, Dharwad, India

    Mahadeva Prasanna

  3. Computer Science & Engineering, Indian Institute of Technology Kanpur, Kanpur, India

    Vinay P. Namboodiri

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Mishra, R., Bhavsar, A. (2020). Analyzing Image Classification via EEG. In: Babu, R.V., Prasanna, M., Namboodiri, V.P. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2019. Communications in Computer and Information Science, vol 1249. Springer, Singapore. https://doi.org/10.1007/978-981-15-8697-2_50

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  • DOI: https://doi.org/10.1007/978-981-15-8697-2_50

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8696-5

  • Online ISBN: 978-981-15-8697-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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