ArticleBrain sciences2020
Lightweight Building of an Electroencephalogram-Based Emotion Detection System.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Secure and efficient implementation of facial emotion detection for smart patient monitoring system.Quantitative biology (Beijing, China) · 2023Article
- Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network.Computational intelligence and neuroscience · 2022Article
- Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task.Entropy (Basel, Switzerland) · 2021Article
- Oscillatory EEG Signatures of Affective Processes during Interaction with Adaptive Computer Systems.Brain sciences · 2020Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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