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A New Approach to Visual Classification Using Concatenated Deep Learning for Multimode Fusion of EEG and Image Data

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Advances in Visual Computing (ISVC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13598))

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Abstract

In this work, we explore various approaches for automated visual classification of multimodal inputs such as EEG and Image data for the same item, focusing on finding an optimal solution. Our new technique examines the fusion of EEG and Image data using a concatenation of deep learning models for classification, where the EEG feature space is encoded with 8-bit-grayscale images. This concatenated-based model achieves a 95% accuracy for the 39 class EEG-ImageNet dataset, setting a new benchmark and surpassing all prior work. Furthermore, we show that it is computationally effective in multimodal classification when human subjects are presented with visual stimuli of objects in three-dimensional real-world space rather than images of the same. These findings will improve machine visual perception and bring it closer to human-learned vision.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Lakehead University, Thunder Bay, Canada

    Alankrit Mishra & Garima Bajwa

Authors
  1. Alankrit Mishra
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  2. Garima Bajwa
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Corresponding author

Correspondence to Garima Bajwa .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. University of Illinois Urbana-Champaign, Urbana, IL, USA

    Bo Li

  3. National University of Singapore, Singapore, Singapore

    Angela Yao

  4. Microsoft Research Asia, Beijing, China

    Yang Liu

  5. University of Missouri, Columbia, MO, USA

    Ye Duan

  6. City University of Hong Kong, Kowloon, Hong Kong

    Manfred Lau

  7. Idaho National Laboratory, Idaho Falls, ID, USA

    Rajiv Khadka

  8. Salesforce, Seattle, WA, USA

    Ana Crisan

  9. Tufts University, Medford, MA, USA

    Remco Chang

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Mishra, A., Bajwa, G. (2022). A New Approach to Visual Classification Using Concatenated Deep Learning for Multimode Fusion of EEG and Image Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13598. Springer, Cham. https://doi.org/10.1007/978-3-031-20713-6_17

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  • DOI: https://doi.org/10.1007/978-3-031-20713-6_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20712-9

  • Online ISBN: 978-3-031-20713-6

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