EEG-based emotion classification using deep belief networks

Wei‐Long Zheng · Jiayi Zhu · Yong Kang Peng · Bao‐Liang Lu

367 citations0/0 files storedIEEE International Conference on Multimedia and Expo (ICME)

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Machine Learning and ELM

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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References

2010Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications 2013Differential entropy feature for EEG-based emotion classification 2013Emotional state classification from EEG data using machine learning approach 2011Multimodal Emotion Recognition in Response to Videos 2009Emotion classification based on gamma-band EEG

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2019A Review, Current Challenges, and Future Possibilities on Emotion Recognition Using Machine Learning and Physiological Signals 2020Time–Frequency Representation and Convolutional Neural Network-Based Emotion Recognition 2019Emotion Recognition from Multiband EEG Signals Using CapsNet 2020EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder 2019Deep Learning for Human Affect Recognition: Insights and New Developments 2014Multimodal emotion recognition using EEG and eye tracking data 2020Multi-channel EEG-based emotion recognition via a multi-level features guided capsule network 2019A snapshot research and implementation of multimodal information fusion for data-driven emotion recognition

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