EEG-based emotion classification using deep belief networks

By Wei-Long Zheng, Jia-Yi Zhu, Yong Peng, Bao-Liang Lu. IEEE International Conference on Multimedia and Expo, 2014

327 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/007464b4564d2fff325bfb85b2a2ee8c20e3fd3e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Wei-Long Zheng
2. Wei Liu
3. Bao-Liang Lu
4. Zhen Cui
5. Wenming Zheng
6. Chunyan Miao
7. Di Wang
8. Peixiang Zhong

In Experimental and Cognitive Psychology

1. Wei-Long Zheng
2. Wei Liu
3. Bao-Liang Lu
4. Zhen Cui
5. Wenming Zheng
6. Chunyan Miao
7. Di Wang
8. Peixiang Zhong

Citations in the corpus

• Identifying Stable Patterns over Time for Emotion Recognition from EEG (2016, background): “GELM yields a much better performance in comparison with other models for face recognition [50] and emotion classification [41].”
• Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks (2015, methodology): “[33] trained a deep belief network with differential entropy features extracted from multichannel EEG as input and achieved the best classification accuracy of 87.”
• Targeting EEG/LFP Synchrony with Neural Nets (2017, methodology): “SEED dataset : This dataset [34] involves repeated tests on 15 subjects.”
• A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series (2017, background): “Less classically and more recently various deep learning approaches have been proposed to learn from EEG data [24, 38, 40, 4] and some of these contributions use a first layer that boils down to a spatial filter [8, 9, 31, 23, 32, 22].”
• EEG-Based Emotion Recognition Using Regularized Graph Neural Networks (2019, background): “The majority of existing classifiers are topology-invariant classifiers such as SVM, k-Nearest Neighbors (KNN), DBN [34] and RNN”
• EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks (2020, methodology): “The commonly used EEG features include the differential entropy (DE) feature [29], [30], the power spectral density (PSD) feature [31],”
• Deep Feature Learning for EEG Recordings (2015, background): “There has also been early work on emotion recognition from EEG using deep neural networks such as described by Jirayucharoensak et al. (2014) and Zheng et al. (2014).”
• EmotionMeter: A Multimodal Framework for Recognizing Human Emotions (2019, methodology): “bers of neurons in the hidden layers were fixed to be the same for the three RBMs when training and were tuned in [200, 150, 100, 90, 70, 50, 30, 20, 15, 10] units using crossvalidation.”