Multi-Source Transfer Learning for EEG Classification Based on Domain Adversarial Neural Network

Liu, Dezheng; Zhang, Jia; Wu, Hanrui; Liu, Siwei; Long, Jinyi

doi:10.1109/tnsre.2022.3219418

Cited by 16 publications

(7 citation statements)

References 64 publications

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“…Beyond traditional GANs, Liang Sui et al [78] have proposed an auxiliary synthesis framework that uses generative models to synthesize supplementary data, thereby providing The framework includes a source-target relevance maximization strategy and self-adjustable generative learning.…”

Section: ) Generative Modelsmentioning

confidence: 99%

Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey

Keutayeva,

Abibullaev

2024

IEEE Access

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This work reviews the critical challenge of data scarcity in developing Transformerbased models for Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), specifically focusing on Motor Imagery (MI) decoding. While EEG-BCIs hold immense promise for applications in communication, rehabilitation, and human-computer interaction, limited data availability hinders the use of advanced deep-learning models such as Transformers. In particular, this paper comprehensively analyzes three key strategies to address data scarcity: data augmentation, transfer learning, and the inherent attention mechanisms of Transformers. Data augmentation techniques artificially expand datasets, enhancing model generalizability by exposing them to a wider range of signal patterns. Transfer learning utilizes pre-trained models from related domains, leveraging their learned knowledge to overcome the limitations of small EEG datasets. By thoroughly reviewing current research and methodologies, this work underscores the importance of these strategies in overcoming data scarcity. It critically examines the limitations imposed by limited datasets and showcases potential solutions being developed to address these challenges. This comprehensive survey, focusing on the intersection of data scarcity and technological advancements, aims to provide a critical analysis of the current state-of-the-art in EEG-BCI development. By identifying research gaps and suggesting future directions, the paper encourages further exploration and innovation in this field. Ultimately, this work aims to contribute to the advancement of more accessible, efficient, and precise EEG-BCI systems by addressing the fundamental challenge of data scarcity.

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Section: ) Generative Modelsmentioning

confidence: 99%

Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey

Keutayeva,

Abibullaev

2024

IEEE Access

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“…Deng et al [41] trained separated classifiers to align each source and target data in ECG classification together with sample-imbalance aware mixing strategy. Similarly, Liu et al [71] trained DANN based on each source domain and the specified target domain. Wei et al [72] aligned the data distribution for each pair of subjects and output by decision fusion.…”

Section: Related Workmentioning

confidence: 99%

SFDA: Domain Adaptation With Source Subject Fusion Based on Multi-Source and Single-Target Fall Risk Assessment

Wu,

Shu,

Song

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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In cross-subject fall risk classification based on plantar pressure, a challenge is that data from different subjects have significant individual information. Thus, the models with insufficient generalization ability can't perform well on new subjects, which limits their application in daily life. To solve this problem, domain adaptation methods are applied to reduce the gap between source and target domain. However, these methods focus on the distribution of the source and the target domain, but ignore the potential correlation among multiple source subjects, which deteriorates domain adaptation performance. In this paper, we proposed a novel method named domain adaptation with subject fusion (SFDA) for fall risk assessment, greatly improving the cross-subject assessment ability. Specifically, SFDA synchronously carries out source target adaptation and multiple source subject fusion by domain adversarial module to reduce source-target gap and distribution distance within source subjects of same class. Consequently, target samples can learn more task-specific features from source subjects to improve the generalization ability. Experiment results show that SFDA achieved mean accuracy of 79.17 % and 73.66 % based on two backbones in a cross-subject classification manner, outperforming the state-of-the-art methods on continuous plantar pressure dataset. This study proves the effectiveness of SFDA and provides a novel tool for implementing cross-subject and few-gait fall risk assessment.

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“…Numerous deep learning-based TL methods and domain adaptation methods have been developed [13][14][15], which are also extensively used in BCI [16][17][18][19]. In MI, data alignment serves as an effective TL method to alleviate the discrepancy between source and target domains [20].…”

Section: Introductionmentioning

confidence: 99%

Transfer learning with data alignment and optimal transport for EEG based motor imagery classification

Chu,

Zhu,

Huang

et al. 2024

J. Neural Eng.

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The non-stationarity of electroencephalogram signals and the variability of different subjects pose major challenges in current Brain-Computer Interfaces research, which requires a time-consuming specific calibration procedure to address. Transfer Learning can make use of data or models from one or more source domains to encourage learning in the target domain, so as to address the challenges of EEG non-stationarity and inter-subject variability. In this paper, a Multi-source domain Transfer Learning Fusion (MTLF) framework is proposed to address the non-stationarity of EEG signals and the complexity of the calibration process. Firstly, the method transforms the source domain data with the resting state segment, so as to decrease the differences between the source domain and the target domain. Then, CSP is used to extract features. After that, Optimal Transport is employed to further refine the source domain data to increase the similarity of the probability distribution between the target and source domains. Finally, an improved transfer learning classifier is employed to classify the target samples. This method does not require the label information of target domain samples and can reduce the calibration workload. The proposed MTLF is assessed on Datasets IIa and IIb of the BCI Competition IV. Compared with seven other algorithms, our method performed relatively the best and achieved mean classification accuracy of 73.69% and 70.83% on Datasets IIa and IIb respectively. Experimental results demonstrate that MTLF can effectively decrease the discrepancy between the source and target domain, and acquire better classification performance on two MI-EEG datasets.

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Multi-Source Transfer Learning for EEG Classification Based on Domain Adversarial Neural Network

Cited by 16 publications

References 64 publications

Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey

Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey

SFDA: Domain Adaptation With Source Subject Fusion Based on Multi-Source and Single-Target Fall Risk Assessment

Transfer learning with data alignment and optimal transport for EEG based motor imagery classification

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