A recurrence quantification analysis-based channel-frequency convolutional neural network for emotion recognition from EEG

Yang, Yuxuan; Gao, Zhong-Ke; Wang, Xinmin; Li, Yanli; Han, Jingwei; Marwan, Norbert; Kurths, Jürgen

doi:10.1063/1.5023857

Cited by 66 publications

(29 citation statements)

References 51 publications

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“…In some studies it is reported that SOBI is capable of functionally separating sources which are physiologically interpretable [24][25][26][27]. SOBI is really robust in low SNRs [27][28][29][30][31][32].…”

Section: Blind Source Separation and Sobimentioning

confidence: 99%

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EEG Source Identification through Phase Space Reconstruction and Complex Networks

Soroush¹

2020

Preprint

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Artifact elimination has become an inseparable part while processing electroencephalogram (EEG) in most brain computer interface (BCI) applications. Scientists have tried to introduce effective and efficient methods which can remove artifacts and also reserve desire information pertaining to brain activity. Blind source separation (BSS) methods have been receiving a great deal of attention in recent decades since they are considered routine and standard signal processing tools and are commonly used to eliminate artifacts and noise. Most studies, mainly EEG-related ones, apply BSS methods in preprocessing sections to achieve better results. On the other hand, BSS methods should be followed by a classifier in order to identify artifactual sources and remove them in next steps. Therefore, artifact identification is always a challenging problem while employing BSS methods. Additionally, removing all detected artifactual components leads to loss of information since some desire information related to neural activity leaks to these sources. So, an approach should be employed to suppress the artifacts and reserve neural activity. In this study, a new hybrid method is proposed to automatically separate and identify electroencephalogram (EEG) sources with the aim of classifying and removing artifacts. Automated source identification is still a challenge. Researchers have always made efforts to propose precise, fast and automated source verification methods. Reliable source identification has always been of great importance. This paper addresses blind source separation based on second order blind identification (SOBI) as it is reportedly one of the most effective methods in EEG source separation problems. Then a new method for source verification is introduced which takes advantage of components phase spaces and their dynamics. A new state space called angle space (AS) is introduced and features are extracted based on the angle plot (AP) and Poincare planes. Identified artifactual sources are eliminated using stationary wavelet transform (SWT). Simulated, semi-simulated and real EEG signals are employed to evaluate the proposed method. Different simulations are performed and performance indices are reported. Results show that the proposed method outperforms most recent studies in this subject.

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Section: Blind Source Separation and Sobimentioning

confidence: 99%

“…These characteristics motivated us to use SOBI in this study. For further information about SOBI algorithm and its utility refer to [29][30][31].…”

Section: Blind Source Separation and Sobimentioning

confidence: 99%

EEG Source Identification through Phase Space Reconstruction and Complex Networks

Soroush¹

2020

Preprint

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“…In 2013, Duan proposed a feature extraction method based on differential entropy, which achieved good results on emotion-based datasets [18]. In the past two years, many researchers have used differential entropy as a feature extraction method to achieve good results on different datasets [19,20]. However, researchers often use multi-channel acquisition equipment when collecting EEG data.…”

Section: Introductionmentioning

confidence: 99%

A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition

Chen

Miao

Yang

et al. 2019

Sensors

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Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, determining how to effectively extract features and reduce the amount of calculation is still the focus of abundant research. Researchers have proposed many EEG feature extraction methods. However, these methods have problems such as high time complexity and insufficient precision. The main purpose of this paper is to introduce an innovative method for obtaining reliable distinguishing features from EEG signals. This feature extraction method combines differential entropy with Linear Discriminant Analysis (LDA) that can be applied in feature extraction of emotional EEG signals. We use a three-category sentiment EEG dataset to conduct experiments. The experimental results show that the proposed feature extraction method can significantly improve the performance of the EEG classification: Compared with the result of the original dataset, the average accuracy increases by 68%, which is 7% higher than the result obtained when only using differential entropy in feature extraction. The total execution time shows that the proposed method has a lower time complexity.

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“…Although those existing machine learning algorithms have found successful applications in the field of emotion recognition, some limitations exist. The limitations include deficient learning inheriting characteristics of training samples [44], and its poor classification accuracy due to the subject dependency of emotion [45]. In addition, the existing machine learning algorithms usually also suffer from severe overfitting [46].…”

Section: Introductionmentioning

confidence: 99%

“…Song et al [16] proposed a Graph CNN (GCNN) to dynamically learn the features and discriminate the relationship between different EEG channels, to improve the EEG emotion recognition. Yang et al [44] integrated a nonlinear method—recurrence quantification analysis (RQA)—into a novel channel-frequency convolutional neural network (CFCNN) for the EEG-based emotion recognition, acquired an accuracy of 92.24%, and also proved strong relationship between emotional process and gamma frequency band. A hierarchical CNN (HCNN) method was adopted by Li et al [46] to distinguish the positive, negative and neutral emotion states, the comparable results with stacked auto-encoder (SAE), SVM and KNN shows HCNN yields the highest accuracy.…”

Section: Introductionmentioning

confidence: 99%

EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

Zeng

Zhang

et al. 2019

Brain Sciences

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Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

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A recurrence quantification analysis-based channel-frequency convolutional neural network for emotion recognition from EEG

Cited by 66 publications

References 51 publications

EEG Source Identification through Phase Space Reconstruction and Complex Networks

EEG Source Identification through Phase Space Reconstruction and Complex Networks

A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition

EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

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