Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification

Zhao, He; Zheng, Qingqing; Ma, Kai; Li, Huiqi; Zheng, Yefeng

doi:10.1109/tnnls.2020.3010780

Cited by 139 publications

(72 citation statements)

References 38 publications

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“…In our review, explicit TL-based methods account for nearly 45% and the remaining works are categorized as implicit TL-based methods. With regard to explicit TL-based methods, there exist two approaches, non-parametric and parametric (i.e., adversarial learning) alignment methods, for a feature space among multiple domains (subjects or sessions) (Jeon et al, 2019 ; Nasiri and Clifford, 2020 ; Özdenizci et al, 2020 ; Zhao H. et al, 2020 ; Wang et al, 2021 ). In Table 3 , we observe that most of the existing adversarial methods employ DANN (Ganin et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

“…In Table 3 , we observe that most of the existing adversarial methods employ DANN (Ganin et al, 2016 ). Further, modified adversarial objective functions, such as WGAN (Arjovsky et al, 2017 ; Gulrajani et al, 2017 ) and LSGAN (Mao et al, 2017 ), have been employed to stabilize the training process in adversarial learning-based TL approaches (Wei et al, 2020b ; Zhao H. et al, 2020 ). In this regard, we expect that numerous variants of DANN (Tzeng et al, 2017 ; Xu et al, 2018 ; Zhang et al, 2018c ; Peng et al, 2019 ; Wang et al, 2019 ) can be applied to DL-based BCI tasks.…”

Section: Discussionmentioning

confidence: 99%

“…Domain adaptation (Wang and Deng, 2018 ) is proposed to diminish the domain shift in other fields, such as computer vision. Owing to its goal, domain adaptation-based approaches have been widely used in DL-based BCIs (Jeon et al, 2019 ; Özdenizci et al, 2020 ; Wei et al, 2020a ; Zhao H. et al, 2020 ). Each subject or session is regarded as one domain in most studies.…”

Section: Advances In Transfer Learningmentioning

confidence: 99%

“…Recently, several methods have shown that the use of only DANN (Ganin et al, 2016 ) has some limitations and challenges (Ma et al, 2019 ; Nasiri and Clifford, 2020 ; Tang and Zhang, 2020 ; Zhao H. et al, 2020 ). First, Zhao et al considered a single subject as a target and the remaining subjects of datasets as source sets; therefore, the domain discriminator was trained to distinguish between the target and the sources.…”

Section: Advances In Transfer Learningmentioning

confidence: 99%

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A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain–Computer Interfaces

Jeon

Jeong

et al. 2021

Front. Hum. Neurosci.

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Brain–computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has had a theoretical/practical impact on BCI research because of its use in learning representations of complex patterns inherent in EEG. Moreover, algorithmic advances in DL facilitate short/zero-calibration in BCI, thereby suppressing the data acquisition phase. Those advancements include data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the so-called data insufficiency problem in other datasets. In this study, we review DL-based short/zero-calibration methods for BCI. Further, we elaborate methodological/algorithmic trends, highlight intriguing approaches in the literature, and discuss directions for further research. In particular, we search for generative model-based and geometric manipulation-based DA methods. Additionally, we categorize TL techniques in DL-based BCIs into explicit and implicit methods. Our systematization reveals advances in the DA and TL methods. Among the studies reviewed herein, ~45% of DA studies used generative model-based techniques, whereas ~45% of TL studies used explicit knowledge transferring strategy. Moreover, based on our literature review, we recommend an appropriate DA strategy for DL-based BCIs and discuss trends of TLs used in DL-based BCIs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Advances In Transfer Learningmentioning

confidence: 99%

Section: Advances In Transfer Learningmentioning

confidence: 99%

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A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain–Computer Interfaces

Jeon

Jeong

et al. 2021

Front. Hum. Neurosci.

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“…1(b)). This joint supervision signal is also widely adopted for the MI detection task due to its efficiency and easy setup [21,22]. However, the center loss is sensitive to the non-Gaussian noise since it is based on the quadratic 𝐿2 norm distance [23].…”

Section: Introductionmentioning

confidence: 99%

Discriminative and Robust Feature Learning for MIBCI-based Disability Rehabilitation

Huang¹,

Zhou²,

Choi³

2021

Preprint

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BackgroundIn the past few years, motor imagery brain-computer interface (MIBCI) has become a valuable assisting technology for the disabled. However, how to effectively improve the motor imagery (MI) classification performance by learning discriminative and robust features is still a challenging problem.MethodsIn this study, we propose a novel loss function, called correntropy-based center loss (CCL), as the supervision signal for the training of the convolutional neural network (CNN) model in the MI classification task. With joint supervision of the softmax loss and CCL, we can train a CNN model to acquire deep discriminative features with large inter-class dispersion and slight intra-class variation. Moreover, the CCL can also effectively decrease the negative effect of the noise during the training, which is essential to accurate MI classification.ResultsWe perform extensive experiments on two well-known public MI datasets, called BCI competition IV-2a and IV-2b, to demonstrate the effectiveness of the proposed loss. The result shows that our CNNs (with such joint supervision) achieve 78.65% and 86.10% on IV-2a and IV-2b and outperform other baseline approaches.ConclusionThe proposed CCL helps the learning process of the CNN model to obtain both discriminative and robust deeply learned features for the MI classification task in the BCI rehabilitation application.

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Deep Domain Adaptation for EEG-Based Cross-Subject Cognitive Workload Recognition

Zhou

Wang

Gong

et al. 2023

Communications in Computer and Information Science

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Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification

Cited by 139 publications

References 38 publications

A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain–Computer Interfaces

A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain–Computer Interfaces

Discriminative and Robust Feature Learning for MIBCI-based Disability Rehabilitation

Deep Domain Adaptation for EEG-Based Cross-Subject Cognitive Workload Recognition

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