EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder

Liu, Junxiu; Wu, Guopei; Luo, Yuling; Qiu, Senhui; Yang, Su; Li, Wei; Bi, Yifei

doi:10.3389/fnsys.2020.00043

Cited by 144 publications

(46 citation statements)

References 44 publications

(52 reference statements)

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“…And their emotion recognition accuracies are listed in Table 1. Liu et al (2020) by combining the CNN, SAE, and DNN and training them separately, the proposed network is shown as an efficient method with a faster convergence than the conventional CNN. And, for the SEED dataset, the best recognition accuracy reaches 96.77%.…”

Section: Classification Of Emotion-related Electroencephalography Signalsmentioning

confidence: 99%

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Cai

Xiao

Cui

et al. 2021

Front. Syst. Neurosci.

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Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject’s emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016–2021.

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Section: Classification Of Emotion-related Electroencephalography Signalsmentioning

confidence: 99%

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Cai

Xiao

Cui

et al. 2021

Front. Syst. Neurosci.

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“…In recent years machine learning (ML) techniques have become important tools in addressing classification tasks that involve medical problems. As examples, we can mention the use of long short-term memory recurrent neural networks (RNNs) to classify diagnoses from pediatric intensive care unit data (Lipton et al, 2015), the use of RNNs and Bayesian models to discriminate patients with ovarian cancer (Mariño et al, 2017;Vázquez et al, 2018), the use of support vector machines (SVMs) for attention deficit hyperactivity disorder prediction (Dai et al, 2012), the application of convolutional neural networks (CNNs) to classifying electroencephalogram (EEG) signals for emotion recognition (Luo et al, 2020), or the combination of multilayer perceptrons and SVMs to diagnose major depressive disorders (Saeedi et al, 2020b).…”

Section: Introductionmentioning

confidence: 99%

An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals

Vázquez

Maghsoudi

Mariño

2021

Front. Syst. Neurosci.

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In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal of diagnostic but, even more importantly, it provides additional information that admits clinical interpretation. It is based on an ML model called random forest that operates on connectivity metrics extracted from the EEG signals. Specifically, we use measures of generalized partial directed coherence (GPDC) and direct directed transfer function (dDTF) to construct the input features to the ML model. The latter allows the identification of the most performance-wise relevant features which, in turn, provide some insights about EEG signals and frequency bands that are associated with schizophrenia. Our preliminary results on real data show that signals associated with the occipital region seem to play a significant role in the diagnosis of the disease. Moreover, although every frequency band might yield useful information for the diagnosis, the beta and theta (frequency) bands provide features that are ultimately more relevant for the ML classifier that we have implemented.

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“…In the feature extraction stage, the time-domain method, frequency-domain method, and nonlinear dynamic method are often used to analyze EEG signals. For example, time-domain analysis can extract the time-dependent features of EEG signals, including sample entropy, statistical features, and principal component analysis [ 15 ]. In frequency-domain analysis, EEG signals can be broken down into δ (1-3 Hz), θ (4-7 Hz), α (8-13 Hz), β (14-30 Hz), and γ bands (31-50 Hz); features can be extracted from each frequency band [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network

Pan

Wei

2021

Computational and Mathematical Methods in Medicine

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Emotion recognition plays an important role in the field of human-computer interaction (HCI). Automatic emotion recognition based on EEG is an important topic in brain-computer interface (BCI) applications. Currently, deep learning has been widely used in the field of EEG emotion recognition and has achieved remarkable results. However, due to the cost of data collection, most EEG datasets have only a small amount of EEG data, and the sample categories are unbalanced in these datasets. These problems will make it difficult for the deep learning model to predict the emotional state. In this paper, we propose a new sample generation method using generative adversarial networks to solve the problem of EEG sample shortage and sample category imbalance. In experiments, we explore the performance of emotion recognition with the frequency band correlation and frequency band separation computational models before and after data augmentation on standard EEG-based emotion datasets. Our experimental results show that the method of generative adversarial networks for data augmentation can effectively improve the performance of emotion recognition based on the deep learning model. And we find that the frequency band correlation deep learning model is more conducive to emotion recognition.

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EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder

Cited by 144 publications

References 44 publications

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals

Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network

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