Lightweight Building of an Electroencephalogram-Based Emotion Detection System
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Computer Science Department, Imam Muhammad ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
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Computer Science Department, King Saud University, Riyadh 11543, Saudi Arabia
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Computer Science Department, Taif University, Taif 26571, Saudi Arabia
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Mechanical Engineering Department, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
*
Author to whom correspondence should be addressed.
Brain Sci. 2020, 10(11), 781; https://doi.org/10.3390/brainsci10110781
Received: 20 August 2020
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Revised: 23 October 2020
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Accepted: 23 October 2020
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Published: 26 October 2020
(This article belongs to the Special Issue Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications)
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.
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Keywords:
brain–computer interface (BCI); electroencephalogram (EEG); EEG-based emotion detection; spiking neural network; NeuCube
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MDPI and ACS Style
Al-Nafjan, A.; Alharthi, K.; Kurdi, H. Lightweight Building of an Electroencephalogram-Based Emotion Detection System. Brain Sci. 2020, 10, 781. https://doi.org/10.3390/brainsci10110781
AMA Style
Al-Nafjan A, Alharthi K, Kurdi H. Lightweight Building of an Electroencephalogram-Based Emotion Detection System. Brain Sciences. 2020; 10(11):781. https://doi.org/10.3390/brainsci10110781
Chicago/Turabian StyleAl-Nafjan, Abeer, Khulud Alharthi, and Heba Kurdi. 2020. "Lightweight Building of an Electroencephalogram-Based Emotion Detection System" Brain Sciences 10, no. 11: 781. https://doi.org/10.3390/brainsci10110781
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