NDLI: Lightweight Building of an Electroencephalogram-Based Emotion Detection System

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Lightweight Building of an Electroencephalogram-Based Emotion Detection System

Content Provider	MDPI
Author	Al-Nafjan, Abeer Alharthi, Khulud Kurdi, Heba
Copyright Year	2020
Description	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.
Starting Page	781
e-ISSN	20763425
DOI	10.3390/brainsci10110781
Journal	Brain Sciences
Issue Number	11
Volume Number	10
Language	English
Publisher	MDPI
Publisher Date	2020-10-26
Access Restriction	Open
Subject Keyword	Brain Sciences Industrial Engineering Brain–computer Interface (bci) Electroencephalogram (eeg) Eeg-based Emotion Detection Spiking Neural Network Neucube
Content Type	Text
Resource Type	Article

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