Cited by 367Emotion and Mood RecognitionEEG and Brain-Computer InterfacesMachine Learning and ELM

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EEG-based emotion classification using deep belief networks

Zheng, Wei-Long, Zhu, Jia-Yi, Peng, Yong, Lu, Bao-Liang·2014·2014 IEEE International Conference on Multimedia and Expo (ICME)

DOI 10.1109/icme.2014.6890166

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Abstract

In recent years, there are many great successes in using deep architectures for unsupervised feature learning from data, especially for images and speech. In this paper, we introduce recent advanced deep learning models to classify two emotional categories (positive and negative) from EEG data. We train a deep belief network (DBN) with differential entropy features extracted from multichannel EEG as input. A hidden markov model (HMM) is integrated to accurately capture a more reliable emotional stage switching. We also compare the performance of the deep models to KNN, SVM and Graph regularized Extreme Learning Machine (GELM). The average accuracies of DBN-HMM, DBN, GELM, SVM, and KNN in our experiments are 87.62%, 86.91%, 85.67%, 84.08%, and 69.66%, respectively. Our experimental results show that the DBN and DBN-HMM models improve the accuracy of EEG-based emotion classification in comparison with the state-of-the-art methods.

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Authors: Zheng, W.-L., Zhu, J.-Y., Peng, Y., & Lu, B.-L.
Affiliations: Shanghai Jiao Tong University (CN)
ORCID: Zheng, Wei-Long
Peng, Yong
Lu, Bao-Liang
Venue: 2014 IEEE International Conference on Multimedia and Expo (ICME)
Publisher: IEEE
Published: 2014-07
Pages: 1-6
DOI: 10.1109/icme.2014.6890166
OpenAlex: W2004104731
Cited by: 367
References: 8
Citations by year: 2026: 10 · 2025: 32 · 2024: 35 · 2023: 37 · 2022: 53 · 2021: 41 · 2020: 39 · 2019: 43 · 2018: 36 · 2017: 22 · 2016: 10 · 2015: 6 · 2014: 3
FWCI: 22.35
Type: conference paper
Language: en
Open access: closed
Locations: IEEE International Conference on Multimedia and Expo (ICME) (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
Keywords: EEG-based emotion recognition, deep belief network, hidden Markov models, differential entropy features, multichannel EEG, positive and negative emotions, support vector machine, k-nearest neighbors

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EEG-based emotion classification using deep belief networks

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