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Video Category Classification Using Wireless EEG

  • Aunnoy K MutasimEmail author
  • Rayhan Sardar Tipu
  • M. Raihanul Bashar
  • M. Ashraful Amin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

EEG BCI HCI Human factor Video Category Classification 

References

  1. 1.
    Oikonomou, V.P., Liaros, G., Georgiadis, K., et al.: Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs. [1602.00904] (2016). https://arxiv.org/abs/1602.00904. Accessed 13 Aug 2017
  2. 2.
    MindWave. http://store.neurosky.com/pages/mindwave. Accessed 13 Aug 2017
  3. 3.
    MUSE™ | Meditation Made Easy. Muse: the brain sensing headband. http://www.choosemuse.com/. Accessed 13 Aug 2017
  4. 4.
    EMOTIV Epoc - 14 Channel Wireless EEG Headset. In: Emotiv. https://www.emotiv.com/epoc/. Accessed 13 Aug 2017
  5. 5.
    Jalilifard, A., Pizzolato, E.B., Islam, M.K.: Emotion classification using single-channel scalp-EEG recording. In: 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC 2016), pp. 845–849. IEEE Press, Orlando (2016). doi: 10.1109/EMBC.2016.7590833
  6. 6.
    Liu, N.-H., Chiang, C.-Y., Chu, H.-C.: Recognizing the degree of human attention using EEG signals from mobile sensors. Sensors 13(8), 10273–10286 (2013). doi: 10.3390/s130810273 CrossRefGoogle Scholar
  7. 7.
    Nine, M.S.Z., Khan, M., Poon, B., Amin, M.A., Yan, H.: Human computer interaction through wireless brain computer interfacing device. In: 9th International Conference on Information Technology and Applications (ICITA 2014) (2014)Google Scholar
  8. 8.
    Paul, S.K., Zulkar Nine, M.S.Q., Hasan, M., Amin, M.A.: Cognitive task classification from wireless EEG. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds.) BIH 2015. LNCS, vol. 9250, pp. 13–22. Springer, Cham (2015). doi: 10.1007/978-3-319-23344-4_2 CrossRefGoogle Scholar
  9. 9.
    Koelstra, S., Mühl, C., Patras, I.: EEG analysis for implicit tagging of video data. In: 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1–6. IEEE Press, Amsterdam (2009). doi: 10.1109/ACII.2009.5349482
  10. 10.
    Soleymani, M., Pantic, M.: Multimedia implicit tagging using EEG signals. In: 2013 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE Press, San Jose (2013). doi: 10.1109/ICME.2013.6607623
  11. 11.
    Hubert, W., Jong-Meyer, R.D.: Autonomic, neuroendocrine, and subjective responses to emotion-inducing film stimuli. Int. J. Psychophysiol. 11(2), 131–140 (1991). doi: 10.1016/0167-8760(91)90005-I CrossRefGoogle Scholar
  12. 12.
    Wang, X.W., Nie, D., Lu, B.L.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94–106 (2014). doi: 10.1016/j.neucom.2013.06.046 CrossRefGoogle Scholar
  13. 13.
    Abujelala, M., Abellanoza, C., Sharma, A., Makedon, F.: Brain-EE: brain enjoyment evaluation using commercial EEG headband. In: Proceedings of the 9th ACM International Conference on Pervasive Technologies Related to Assistive Environments, p. 33. ACM, Island of Corfu, Greece (2016). doi: 10.1145/2910674.2910691
  14. 14.
    Galway, L., McCullagh, P., Lightbody, G., Brennan, C., Trainor, D.: The potential of the brain-computer interface for learning: a technology review. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 1554–1559. IEEE Press, Liverpool (2015). doi: 10.1109/CIT/IUCC/DASC/PICOM.2015.234
  15. 15.
    Karydis, T., Aguiar, F., Foster, S.L., Mershin, A.: Performance characterization of self-calibrating protocols for wearable EEG applications. In: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, p. 38. ACM, New York (2015). doi: 10.1145/2769493.2769533
  16. 16.
    Experiment 1 Version 2. YouTube (2016). https://www.youtube.com/watch?v=elTcEnCOMc0&feature=youtu.be. Accessed 13 Aug 2017
  17. 17.
    Giglia, G., Brighina, F., Rizzo, S., Puma, A., Indovino, S., Maccora, S., Baschi, R., Cosentino, G., Fierro, B.: Anodal transcranial direct current stimulation of the right dorsolateral prefrontal cortex enhances memory-guided responses in a visuospatial working memory task. Func. Neurol. 29(3), 189–193 (2014). doi: 10.11138/FNeur/2014.29.3.189 CrossRefGoogle Scholar
  18. 18.
    Howard, M.W., Rizzuto, D.S., Caplan, J.B., Madsen, J.R., Lisman, J., Aschenbrenner-Scheibe, R., Schulze-Bonhage, A., Kahana, M.J.: Gamma oscillations correlate with working memory load in humans. Cereb. Cortex 13(12), 1369–1374 (2003). doi: 10.1093/cercor/bhg084 CrossRefGoogle Scholar
  19. 19.
    Linden, D.E.J., Oosterhof, N.N., Klein, C., Downing, P.E.: Mapping brain activation and information during category-specific visual working memory. J. Neurophysiol. 107(2), 628–639 (2011). doi: 10.1152/jn.00105.2011 CrossRefGoogle Scholar
  20. 20.
    Roux, F., Wibral, M., Mohr, H.M., et al.: Gamma-Band Activity in Human Prefrontal Cortex Codes for the Number of Relevant Items Maintained in Working Memory. J. Neurosci. 32, 12411–12420 (2012). doi: 10.1523/jneurosci.0421-12.2012 CrossRefGoogle Scholar
  21. 21.
    Kanayama, N., Sato, A., Ohira, H.: Crossmodal effect with rubber hand illusion and gamma-band activity. Psychophysiology 44(3), 392–402 (2007). doi: 10.1111/j.1469-8986.2007.00511.x CrossRefGoogle Scholar
  22. 22.
    Kisley, M.A., Cornwell, Z.M.: Gamma and beta neural activity evoked during a sensory gating paradigm: effects of auditory somatosensory and cross-modal stimulation. Clin. Neurophysiol. 117(11), 2549–2563 (2006). doi: 10.1016/j.clinph.2006.08.003 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Aunnoy K Mutasim
    • 1
    Email author
  • Rayhan Sardar Tipu
    • 1
  • M. Raihanul Bashar
    • 1
  • M. Ashraful Amin
    • 1
  1. 1.Computer Vision and Cybernetics Group, Department of Computer Science and EngineeringIndependent University, BangladeshDhakaBangladesh

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