Preference Classification Using Electroencephalography (EEG) and Deep Learning

Authors

  • Jason Teo Faculty of Computing & Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia.
  • Chew Lin Hou Faculty of Computing & Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia.
  • James Mountstephens Faculty of Computing & Informatics, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia.

Keywords:

Deep Learning, Electroencephalography, Emotion Classification, Preference Recognition, Visual Aesthetics,

Abstract

Electroencephalogram (EEG)-based emotion classification is rapidly becoming one of the most intensely studied areas of brain-computer interfacing (BCI). The ability to passively identify yet accurately correlate brainwaves with our immediate emotions opens up truly meaningful and previously unattainable human-computer interactions such as in forensic neuroscience, rehabilitative medicine, affective entertainment and neuro-marketing. One particularly useful yet rarely explored areas of EEG-based emotion classification is preference recognition [1], which is simply the detection of like versus dislike. Within the limited investigations into preference classification, all reported studies were based on musicallyinduced stimuli except for a single study which used 2D images. We present two EEG-based preference classification studies: using (1) kNN for a 10-subject EEG classification problem; (2) deep learning for an expanded 16-subject EEG classification problem. We show that inter-subject variability introduces significant classification problems when larger cohorts of test subjects are used and that deep learning shows promising results in terms of addressing this inter-subject variability problem in EEG-based preference classification.

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Published

2018-02-26

How to Cite

Teo, J., Hou, C. L., & Mountstephens, J. (2018). Preference Classification Using Electroencephalography (EEG) and Deep Learning. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-11), 87–91. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3855

Issue

Vol. 10 No. 1-11: Fostering National Development Through Science and Technology II

Section

Articles

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