Journal ArticleDOI
Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks
Wei-Long Zheng,Bao-Liang Lu +1 more
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TLDR
The experiment results show that neural signatures associated with different emotions do exist and they share commonality across sessions and individuals, and the performance of deep models with shallow models is compared.Abstract:
To investigate critical frequency bands and channels, this paper introduces deep belief networks (DBNs) to constructing EEG-based emotion recognition models for three emotions: positive, neutral and negative. We develop an EEG dataset acquired from 15 subjects. Each subject performs the experiments twice at the interval of a few days. DBNs are trained with differential entropy features extracted from multichannel EEG data. We examine the weights of the trained DBNs and investigate the critical frequency bands and channels. Four different profiles of 4, 6, 9, and 12 channels are selected. The recognition accuracies of these four profiles are relatively stable with the best accuracy of 86.65%, which is even better than that of the original 62 channels. The critical frequency bands and channels determined by using the weights of trained DBNs are consistent with the existing observations. In addition, our experiment results show that neural signatures associated with different emotions do exist and they share commonality across sessions and individuals. We compare the performance of deep models with shallow models. The average accuracies of DBN, SVM, LR, and KNN are 86.08%, 83.99%, 82.70%, and 72.60%, respectively.read more
Citations
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Book ChapterDOI
Implementation of EEG Emotion Recognition System Based on Hierarchical Convolutional Neural Networks
TL;DR: This paper introduces hierarchical convolutional neural networks (HCNN) to implement the EEG-based emotion classifier (positive, negative and neutral) in a movie-watching task and finds Beta and Gamma waves play the key role in emotion recognition.
Proceedings ArticleDOI
An AI-Edge Platform with Multimodal Wearable Physiological Signals Monitoring Sensors for Affective Computing Applications
TL;DR: An AI-edge emotion recognition platform using multiple wearable physiological signals sensors: Electroencephalogram (EEG), electrocardiogram (ECG), and photoplethysmogram (PPG) sensors to implement realtime monitoring and classification on edge is developed.
Journal ArticleDOI
EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG With an Application to Emotion Recognition
TL;DR: In this article, a hybrid unsupervised deep convolutional recurrent generative adversarial network based on EEG feature characterization and fusion model is proposed, which is termed as EEGFuseNet.
Journal ArticleDOI
Instance-Adaptive Graph for EEG Emotion Recognition
TL;DR: A novel instance-adaptive graph method (IAG), which employs a more flexible way to construct graphic connections so as to present different graphic representations determined by different input instances, which achieves the state-of-the-art performance.
Journal ArticleDOI
EEG data augmentation for emotion recognition with a multiple generator conditional Wasserstein GAN
TL;DR: Experimental results demonstrate that the artificial data that are generated by the proposed model can effectively improve the performance of emotion classification models that are based on EEG.
References
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