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International Conference on Brain Informatics

BI 2017: Brain Informatics pp 39–48Cite as

Video Category Classification Using Wireless EEG

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10654))

Abstract

In this paper, we present a novel idea where we analyzed EEG signals to classify what type of video a person is watching which we believe is the first step of a BCI based video recommender system. For this, we setup an experiment where 13 subjects were shown three different types of videos. To be able to classify each of these videos from the EEG data of the subjects with a very good classification accuracy, we carried out experiments with several state-of-the-art algorithms for each of the submodules (pre-processing, feature extraction, feature selection and classification) of the Signal Processing module of a BCI system in order to find out what combination of algorithms best predicts what type of video a person is watching. We found, the best results (80.0% with 32.32 ms average total execution time per subject) are obtained when data of channel AF8 are used (i.e. data recorded from the electrode located at the right frontal lobe of the brain). The combination of algorithms that achieved this highest average accuracy of 80.0% are FIR Least Squares, Welch Spectrum, Principal Component Analysis and Adaboost for the submodules pre-processing, feature extraction, feature selection and classification respectively.

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Author information

Authors and Affiliations

  1. Computer Vision and Cybernetics Group, Department of Computer Science and Engineering, Independent University, Bangladesh, Dhaka, Bangladesh

    Aunnoy K Mutasim, Rayhan Sardar Tipu, M. Raihanul Bashar & M. Ashraful Amin

Authors
  1. Aunnoy K Mutasim
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  2. Rayhan Sardar Tipu
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  3. M. Raihanul Bashar
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  4. M. Ashraful Amin
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Corresponding author

Correspondence to Aunnoy K Mutasim .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Yi Zeng

  2. Beijing Normal University, Beijing, China

    Yong He

  3. KTH Royal Institute of Technology and Karolinska Institute, Stockholm, Sweden

    Jeanette Hellgren Kotaleski

  4. University of California, San Diego, San Diego, California, USA

    Maryann Martone

  5. Chinese Academy of Sciences, Beijing, China

    Bo Xu

  6. Allen Institute for Brain Science, Seattle, Washington, USA

    Hanchuan Peng

  7. Wuhan National Lab Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Qingming Luo

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Mutasim, A.K., Tipu, R.S., Raihanul Bashar, M., Ashraful Amin, M. (2017). Video Category Classification Using Wireless EEG. In: Zeng, Y., et al. Brain Informatics. BI 2017. Lecture Notes in Computer Science(), vol 10654. Springer, Cham. https://doi.org/10.1007/978-3-319-70772-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-70772-3_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70771-6

  • Online ISBN: 978-3-319-70772-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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