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Journal ArticleDOI

Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

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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.

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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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Proceedings Article

ImageNet Classification with Deep Convolutional Neural Networks

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Reducing the Dimensionality of Data with Neural Networks

TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI

A fast learning algorithm for deep belief nets

TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Book

Neural Networks And Learning Machines

Simon Haykin
TL;DR: Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together.
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