Selective Cross-Subject Transfer Learning Based on Riemannian Tangent Space for Motor Imagery Brain-Computer Interface

Xu, Yilu; Huang, Xin; Lan, Quan

doi:10.3389/fnins.2021.779231

Cited by 17 publications

(14 citation statements)

References 31 publications

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“…Jeng et al [18] introduced a low-dimensional representation-based TL framework for MI decoding based on tensor decomposition. Xu et al [15] proposed an instance-based selective TL approach for MI in the Riemannian tangent space, which utilizes labeled samples from the source and target subjects. • Deep TL: It has also been well studied in MI and aBCI.…”

Section: Transfer Learning For MI and Abcimentioning

confidence: 99%

“…Transfer learning (TL) [11] is a promising approach to alleviate this problem. Various TL approaches have been proposed for BCI in the last decade, e.g., adaptive CSP [12], data alignment [13], [14], instance-based TL [15], [16], feature-based TL [17], [18], and deep TL [19]. For aBCIs, existing TL approaches mainly include feature-based TL [20] and adversarial-based deep TL [21], [22].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Source Decentralized Transfer for Privacy-Preserving BCIs

Zhang

Wang

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Transfer learning, which utilizes labeled source domains to facilitate the learning in a target model, is effective in alleviating high intra-and inter-subject variations in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Existing transfer learning approaches usually use the source subjects' EEG data directly, leading to privacy concerns. This paper considers a decentralized privacy-preserving transfer learning scenario: there are multiple source subjects, whose data and computations are kept local, and only the parameters or predictions of their pre-trained models can be accessed for privacyprotection; then, how to perform effective cross-subject transfer for a new subject with unlabeled EEG trials? We propose an offline unsupervised multi-source decentralized transfer (MSDT) approach, which first generates a pre-trained model from each source subject, and then performs decentralized transfer using the source model parameters (in gray-box settings) or predictions (in black-box settings). Experiments on two datasets from two BCI paradigms, motor imagery and affective BCI, demonstrated that MSDT outperformed several existing approaches, which do not consider privacy-protection at all. In other words, MSDT achieved both high privacy-protection and better classification performance.

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Section: Transfer Learning For MI and Abcimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Source Decentralized Transfer for Privacy-Preserving BCIs

Zhang

Wang

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…However, the regularization parameters, which were used to evaluate the differences between the labelled source and target domains, were often manually set or were obtained by means of cross-validation. Recently, since affine transformation can make the covariance matrices of EEG data from different domains close, RA-based supervised transfer learning algorithms have received widespread attention in different EEG-based BCIs [40][41][42][43]. Zanini first performed RA for each domain using the Riemannian mean of its resting trials as the reference matrix and then concatenated all aligned matrices from the labelled domains to train a minimum distance to mean (MDM) classifier based on Riemannian Gaussian distributions [44].…”

Section: Related Workmentioning

confidence: 99%

Supervised and Semisupervised Manifold Embedded Knowledge Transfer in Motor Imagery-Based BCI

Yin

et al. 2022

Computational Intelligence and Neuroscience

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A long calibration procedure limits the use in practice for a motor imagery (MI)-based brain-computer interface (BCI) system. To tackle this problem, we consider supervised and semisupervised transfer learning. However, it is a challenge for them to cope with high intersession/subject variability in the MI electroencephalographic (EEG) signals. Based on the framework of unsupervised manifold embedded knowledge transfer (MEKT), we propose a supervised MEKT algorithm (sMEKT) and a semisupervised MEKT algorithm (ssMEKT), respectively. sMEKT only has limited labelled samples from a target subject and abundant labelled samples from multiple source subjects. Compared to sMEKT, ssMEKT adds comparably more unlabelled samples from the target subject. After performing Riemannian alignment (RA) and tangent space mapping (TSM), both sMEKT and ssMEKT execute domain adaptation to shorten the differences among subjects. During domain adaptation, to make use of the available samples, two algorithms preserve the source domain discriminability, and ssMEKT preserves the geometric structure embedded in the labelled and unlabelled target domains. Moreover, to obtain a subject-specific classifier, sMEKT minimizes the joint probability distribution shift between the labelled target and source domains, whereas ssMEKT performs the joint probability distribution shift minimization between the unlabelled target domain and all labelled domains. Experimental results on two publicly available MI datasets demonstrate that our algorithms outperform the six competing algorithms, where the sizes of labelled and unlabelled target domains are variable. Especially for the target subjects with 10 labelled samples and 270/190 unlabelled samples, ssMEKT shows 5.27% and 2.69% increase in average accuracy on the two abovementioned datasets compared to the previous best semisupervised transfer learning algorithm (RA-regularized common spatial patterns-weighted adaptation regularization, RA-RCSP-wAR), respectively. Therefore, our algorithms can effectively reduce the need of labelled samples for the target subject, which is of importance for the MI-based BCI application.

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“…Nowadays, a robotic arm can be controlled via measuring and recording electroencephalogram (EEG) signals, which is progressively used in BCI applications of non-invasive modalities for its practicality and security (Gao et al, 2003 ; Kumar and Reddy, 2020 ). Several commonly used EEG paradigms include steady-state visual evoked potential (SSVEP), P300 (Farwell et al, 2014 ; Yin et al, 2016 ), and motor imagery (MI) (Song and Kim, 2019 ; Xu et al, 2021 ). Compared to the EEG paradigms of P300 and MI, the SSVEP-based BCI system is preferable in robotic arms control owing to the little training and relatively high recognition accuracy (Ge et al, 2019 ; Chen et al, 2020 ; Zhang et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Control of a Robotic Arm With an Optimized Common Template-Based CCA Method for SSVEP-Based BCI

Peng

Zhao

et al. 2022

Front. Neurorobot.

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Recently, the robotic arm control system based on a brain-computer interface (BCI) has been employed to help the disabilities to improve their interaction abilities without body movement. However, it's the main challenge to implement the desired task by a robotic arm in a three-dimensional (3D) space because of the instability of electroencephalogram (EEG) signals and the interference by the spontaneous EEG activities. Moreover, the free motion control of a manipulator in 3D space is a complicated operation that requires more output commands and higher accuracy for brain activity recognition. Based on the above, a steady-state visual evoked potential (SSVEP)-based synchronous BCI system with six stimulus targets was designed to realize the motion control function of the seven degrees of freedom (7-DOF) robotic arm. Meanwhile, a novel template-based method, which builds the optimized common templates (OCTs) from various subjects and learns spatial filters from the common templates and the multichannel EEG signal, was applied to enhance the SSVEP recognition accuracy, called OCT-based canonical correlation analysis (OCT-CCA). The comparison results of offline experimental based on a public benchmark dataset indicated that the proposed OCT-CCA method achieved significant improvement of detection accuracy in contrast to CCA and individual template-based CCA (IT-CCA), especially using a short data length. In the end, online experiments with five healthy subjects were implemented for achieving the manipulator real-time control system. The results showed that all five subjects can accomplish the tasks of controlling the manipulator to reach the designated position in the 3D space independently.

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Selective Cross-Subject Transfer Learning Based on Riemannian Tangent Space for Motor Imagery Brain-Computer Interface

Cited by 17 publications

References 31 publications

Multi-Source Decentralized Transfer for Privacy-Preserving BCIs

Multi-Source Decentralized Transfer for Privacy-Preserving BCIs

Supervised and Semisupervised Manifold Embedded Knowledge Transfer in Motor Imagery-Based BCI

Control of a Robotic Arm With an Optimized Common Template-Based CCA Method for SSVEP-Based BCI

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