Lightweight Building of an Electroencephalogram-Based Emotion Detection System
Name
brainsci-10-00781.pdf
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7.9 MB
Format
Adobe PDF
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b261ae7d932be95a8843e5d22493700c
Author(s)
Kurdi, Heba A.
Date Issued
October 26, 2020
Journal
Brain Sciences
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Al-Nafjan, Abeer, Khulud Alharthi and Heba Kurdi. “Lightweight Building of an Electroencephalogram-Based Emotion Detection System.” Brain Sciences, 10, 11 (October 2020): 781 © 2020 The Author(s)
Version
Final published version
Abstract
Brain–computer interface (BCI) technology provides a direct interface between the brain and an external device. BCIs have facilitated the monitoring of conscious brain electrical activity via electroencephalogram (EEG) signals and the detection of human emotion. Recently, great progress has been made in the development of novel paradigms for EEG-based emotion detection. These studies have also attempted to apply BCI research findings in varied contexts. Interestingly, advances in BCI technologies have increased the interest of scientists because such technologies’ practical applications in human–machine relationships seem promising. This emphasizes the need for a building process for an EEG-based emotion detection system that is lightweight, in terms of a smaller EEG dataset size and no involvement of feature extraction methods. In this study, we investigated the feasibility of using a spiking neural network to build an emotion detection system from a smaller version of the DEAP dataset with no involvement of feature extraction methods while maintaining decent accuracy. The results showed that by using a NeuCube-based spiking neural network, we could detect the valence emotion level using only 60 EEG samples with 84.62% accuracy, which is a comparable accuracy to that of previous studies.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/brainsci10110781
