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
Reads0
Chats0
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
More filters
Proceedings ArticleDOI
A Multi-view Spectral-Spatial-Temporal Masked Autoencoder for Decoding Emotions with Self-supervised Learning
TL;DR: Extensive experiments on two open emotional EEG datasets demonstrate that the proposed MV-SSTMA achieves state-of-the-art performance on emotion recognition, and under the abnormal circumstance of missing channels, the proposed model can still effectively recognize emotions.
Peer ReviewDOI
Moving From Narrative to Interactive Multi-Modal Sentiment Analysis: A Survey
TL;DR: For a comprehensive overview of the state-of-the-art techniques in multi-modal sentiment analysis, specifically focusing on various sentiment interaction tasks, see as mentioned in this paper for a survey.
Proceedings ArticleDOI
The effect of time window length on dynamic brain network analysis under various emotional conditions
TL;DR: In this article , the effect of time window length is revealed on dynamic brain network investigation, a quantitative pipeline is proposed based on brain network extraction and community mining in this paper, the EEG data of healthy subjects are recorded under sadness, happiness and neutral emotions that induced through video stimulation.
Journal ArticleDOI
Depression Recognition From EEG Signals Using an Adaptive Channel Fusion Method via Improved Focal Loss
Jian Shen,Yanan Zhang,Huajian Liang,Zeguang Zhao,Kexin Zhu,Kun Qian,Qunxi Dong,Xiaowei Zhang,Bin Hu +8 more
TL;DR: Wang et al. as mentioned in this paper proposed an adaptive channel fusion method via improved focal loss (FL) functions for depression recognition based on EEG signals to effectively address the challenge of sufficiently optimizing the spatial information derived from the multichannel space of EEG signals.
Journal ArticleDOI
CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition
Weidong Li,Tien-Ping Tan +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a CNN Interactive Transformer Network (CIT-EmotionNet) for EEG Emotion Recognition, which efficiently integrates global and local features of EEG signals.
References
More filters
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.
Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Journal ArticleDOI
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
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.