Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks
TLDR
A hybrid deep neural network is constructed to deal with the EEG MFI sequences to recognize human emotional states where the hybridDeep Neural Networks combined the Convolution Neural Networks (CNN) and Long Short-Term-Memory (LSTM) Recurrent Neural networks (RNN).Abstract:
The aim of this study is to recognize human emotions by electroencephalographic (EEG) signals. The innovation of our research methods involves two aspects: First, we integrate the spatial characteristics, frequency domain, and temporal characteristics of the EEG signals, and map them to a two-dimensional image. With these images, we build a series of EEG Multidimensional Feature Image (EEG MFI) sequences to represent the emotion variation with EEG signals. Second, we construct a hybrid deep neural network to deal with the EEG MFI sequences to recognize human emotional states where the hybrid deep neural network combined the Convolution Neural Networks (CNN) and Long Short-Term-Memory (LSTM) Recurrent Neural Networks (RNN). Empirical research is carried out with the open-source dataset DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) using our method, and the results demonstrate the significant improvements over current state-of-the-art approaches in this field. The average emotion classification accuracy of each subject with CLRNN (the hybrid neural networks that we proposed in this study) is 75.21%.read more
Citations
More filters
Journal ArticleDOI
Deep learning for electroencephalogram (EEG) classification tasks: a review.
TL;DR: Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.
Journal ArticleDOI
Human Emotion Recognition: Review of Sensors and Methods.
TL;DR: This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity and proposes their classification.
Journal ArticleDOI
Emotion Recognition from Multiband EEG Signals Using CapsNet
TL;DR: Experiments conducted on the dataset for emotion analysis using EEG, physiological, and video signals (DEAP) indicate that the proposed method outperforms most of the common models and the capsule network was more suitable for mining and utilizing the three correlation characteristics.
Proceedings ArticleDOI
Emotion Recognition from Multi-Channel EEG through Parallel Convolutional Recurrent Neural Network
TL;DR: Results indicate that the proposed pre-processing method can increase emotion recognition accuracy by 32% approximately and the model achieves a high performance with a mean accuracy of 90.80% and 91.03% on valence and arousal classification task respectively.
Journal ArticleDOI
EEG-based Emotion Recognition via Channel-wise Attention and Self Attention
TL;DR: In this paper, an attention-based convolutional recurrent neural network (ACRNN) was proposed to extract more discriminative features from EEG signals and improve the accuracy of emotion recognition.
References
More filters
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Learning long-term dependencies with gradient descent is difficult
TL;DR: This work shows why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captured increases, and exposes a trade-off between efficient learning by gradient descent and latching on information for long periods.
Journal Article
The ten-twenty electrode system of the international federation
TL;DR: During the First International EEG Congress, London in 1947, it was recommended that Dr. Herbert H. Jasper study methods to standardize the placement of electrodes used in EEG (Jasper 1958).
Journal ArticleDOI
A proposed mechanism of emotion
TL;DR: The following discussion presents some anatomic, clinical and experimental data dealing with the hypothalamus, the gyrus cinguli, the hippocampus and their interconnections, which are proposed as representing theoretically the anatomic basis of the emotions.
Journal ArticleDOI
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Sander Koelstra,Christian Mühl,Mohammad Soleymani,Jong-Seok Lee,Ashkan Yazdani,Touradj Ebrahimi,Thierry Pun,Anton Nijholt,Ioannis Patras +8 more
TL;DR: A multimodal data set for the analysis of human affective states was presented and a novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool.
Related Papers (5)
Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks
Wei-Long Zheng,Bao-Liang Lu +1 more