A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition

Li, Yang; Zheng, Wenming; Zong, Yuan; Cui, Zhen; Zhang, Tong; Zhou, Xiaoyan

doi:10.1109/taffc.2018.2885474

Cited by 201 publications

(133 citation statements)

References 41 publications

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“…Thus, each individual is not likely to share the common EEG distributions that correlate to the same emotional states. Researchers have highlighted the significant challenges posed by intersubject classification in affective computing [ 140 , 142 – 147 ]. Lin describes that for a subject-dependent exercise (intersubject classification) to work well, the class distributions between individuals have to be similar to some extent.…”

Section: Examining Previous Studiesmentioning

confidence: 99%

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

Suhaimi

Mountstephens

Teo

2020

Computational Intelligence and Neuroscience

241

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Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful “emotional” interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.

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Section: Examining Previous Studiesmentioning

confidence: 99%

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

Suhaimi

Mountstephens

Teo

2020

Computational Intelligence and Neuroscience

241

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“…Method ACC/STD(%) SVM (Suykens and Vandewalle 1999) 56.73 / 16.29 KPCA (Schölkopf and Müller 1998) 61.28 / 14.62 TCA (Pan et al 2011) 63.64 / 14.88 TPT (Sangineto et al 2014) 76.31 / 15.89 DANN (Ganin et al 2016) 75.08 / 11.18 DGCNN (Song et al 2018) 79.95 / 09.02 BiDANN (Li et al 2018b) 83.28 / 09.60 BiDANN-S (Li et al 2018d) 84.14 / 06.87 R2G-STNN (Li et al 2019) 84 Table 2, our IAG achieves better classification result, which is 5.04% and 8.04% higher than graph-based methods, i.e., DGCNN and GCNN, respectively. Although they are all graphbased methods, our self-adaptive structure is more effective to characterize the intrinsic relationships between different EEG channels.…”

Section: Experiments Resultsmentioning

confidence: 99%

Instance-Adaptive Graph for EEG Emotion Recognition

Song¹,

Liu²,

Zheng³

et al. 2020

AAAI

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To tackle the individual differences and characterize the dynamic relationships among different EEG regions for EEG emotion recognition, in this paper, we propose 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. To fit the different EEG pattern, we employ an additional branch to characterize the intrinsic dynamic relationships between different EEG channels. To give a more precise graphic representation, we design the multi-level and multi-graph convolutional operation and the graph coarsening. Furthermore, we present a type of sparse graphic representation to extract more discriminative features. Experiments on two widely-used EEG emotion recognition datasets are conducted to evaluate the proposed model and the experimental results show that our method achieves the state-of-the-art performance.

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“…However, the aforementioned methods still primarily rely on the quality of handcrafted features from EEG signals. On the contrary, Li et al proposed a new deep learning model for EEG emotion recognition between different subjects: the bi-hemisphere domain adaptation network (DANN), which extracts common features between different subjects [ 26 ]. It introduces a domain discriminator and a feature extractor to mitigate the variability of the learned feature representation and extracts commonly shared EEG features by minimizing the distribution discrepancy between the EEG signals from the source and target subjects.…”

Section: Introductionmentioning

confidence: 99%

Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding

Tang

Zhang

2020

Entropy

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Decoding motor imagery (MI) electroencephalogram (EEG) signals for brain-computer interfaces (BCIs) is a challenging task because of the severe non-stationarity of perceptual decision processes. Recently, deep learning techniques have had great success in EEG decoding because of their prominent ability to learn features from raw EEG signals automatically. However, the challenge that the deep learning method faces is that the shortage of labeled EEG signals and EEGs sampled from other subjects cannot be used directly to train a convolutional neural network (ConvNet) for a target subject. To solve this problem, in this paper, we present a novel conditional domain adaptation neural network (CDAN) framework for MI EEG signal decoding. Specifically, in the CDAN, a densely connected ConvNet is firstly applied to obtain high-level discriminative features from raw EEG time series. Then, a novel conditional domain discriminator is introduced to work as an adversarial with the label classifier to learn commonly shared intra-subjects EEG features. As a result, the CDAN model trained with sufficient EEG signals from other subjects can be used to classify the signals from the target subject efficiently. Competitive experimental results on a public EEG dataset (High Gamma Dataset) against the state-of-the-art methods demonstrate the efficacy of the proposed framework in recognizing MI EEG signals, indicating its effectiveness in automatic perceptual decision decoding.

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A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition

Cited by 201 publications

References 41 publications

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

Instance-Adaptive Graph for EEG Emotion Recognition

Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding

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