Bilinear Regularized Locality Preserving Learning on Riemannian Graph for Motor Imagery BCI

Xie, Xiaofeng; Yu, Zhu Liang; Gu, Zhenghui; Zhang, Jun; Cen, Ling; Li, Yuanqing

doi:10.1109/tnsre.2018.2794415

Cited by 30 publications

(10 citation statements)

References 36 publications

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“…Region covariance descriptor has been applied in the computer vision and brain computer interface problem [30][31][32]. Deng et al introduced the descriptor to HSI processing [26][27][28].…”

Section: Region Covariance Descriptormentioning

confidence: 99%

Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification

Chen

et al. 2019

Applied Sciences

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Manifold learning is a powerful dimensionality reduction tool for a hyperspectral image (HSI) classification to relieve the curse of dimensionality and to reveal the intrinsic low-dimensional manifold. However, a specific characteristic of HSIs, i.e., irregular spatial dependency, is not taken into consideration in the method design, which can yield many spatially homogenous subregions in an HSI scence. Conventional manifold learning methods, such as a locality preserving projection (LPP), pursue a unified projection on the entire HSI, while neglecting the local homogeneities on the HSI manifold caused by those spatially homogenous subregions. In this work, we propose a novel multiscale superpixelwise LPP (MSuperLPP) for HSI classification to overcome the challenge. First, we partition an HSI into homogeneous subregions with a multiscale superpixel segmentation. Then, on each scale, subregion specific LPPs and the associated preliminary classifications are performed. Finally, we aggregate the classification results from all scales using a decision fusion strategy to achieve the final result. Experimental results on three real hyperspectral data sets validate the effectiveness of our method.

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Section: Region Covariance Descriptormentioning

confidence: 99%

Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification

Chen

et al. 2019

Applied Sciences

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“…Riemannian manifold-based methods, such as Riemannian CSP [13], tangent space linear discriminant analysis (TSLDA) [14], bilinear sub-manifold learning (BSML) [19] and bilinear regularized locality preserving (BRLP) [20], attempt to project EEG signals from Euclidean space into Riemannian manifolds, where the relationship of samples is expressed by the Riemannian distance. Many efficient Riemannian manifold tools, such as the Riemannian mean and tangent space, can be applied to enhance the classification performance of motor imagery.…”

Section: Introductionmentioning

confidence: 99%

Multiple graph fusion based on Riemannian geometry for motor imagery classification

Xie

Zou

et al. 2022

Appl Intell

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In motor imagery-based brain-computer interfaces (BCIs), the spatial covariance features of electroencephalography (EEG) signals that lie on Riemannian manifolds are used to enhance the classification performance of motor imagery BCIs. However, the problem of subject-specific bandpass frequency selection frequently arises in Riemannian manifold-based methods. In this study, we propose a multiple Riemannian graph fusion (MRGF) model to optimize the subject-specific frequency band for a Riemannian manifold. After constructing multiple Riemannian graphs corresponding to multiple bandpass frequency bands, graph embedding based on bilinear mapping and graph fusion based on mutual information were applied to simultaneously extract the spatial and spectral features of the EEG signals from Riemannian graphs. Furthermore, with a support vector machine (SVM) classifier performed on learned features, we obtained an efficient algorithm, which achieves higher classification performance on various datasets, such as BCI competition IIa and in-house BCI datasets. The proposed methods can also be used in other classification problems with sample data in the form of covariance matrices.

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“…In recent years, two typical and general approaches make important achievements in EEG-MI recognition and brain-machine interface (BMI): optimizing the hand-crafted features and extracting the ERS/ERD features by deep learning. For the former approach, common spatial pattern (CSP) filters and Riemannian Manifold [5][6][7][8] are two popular and effective methods. The CSP method is optimal for discrimination of the filtered time series data, and it forms a low dimensional spatial-subspace for the acquired multi-channel EEG data and derives a covariance matrix for each MI class [9].…”

Section: Introductionmentioning

confidence: 99%

“…The Riemannian based dimension reduction algorithm is derived to construct a low-dimensional embedding from high-dimensional Riemannian manifold. Li's group used the geodesic distance of Riemannian manifold to determine the adjacency and weight in Riemannian graph, and then proposed bilinear regularized locality preserving (BRLP) to address the problem of high dimensions frequently arising from BMIs [6]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Dual Head and Dual Attention in Deep Learning for End-to-End EEG Motor Imagery Classification

Yao

Ni³

2021

Applied Sciences

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Event-Related Desynchronization (ERD) or Electroencephalogram (EEG) wavelet is essential for motor imagery (MI) classification and BMI (Brain–Machine Interface) application. However, it is difficult to recognize multiple tasks for non-trained subjects that are indispensable for the complexities of the task or the uncertainties in the environment. The subject-independent scenario, where an inter-subject trained model can be directly applied to new users without precalibration, is particularly desired. Therefore, this paper focuses on an effective attention mechanism which can be applied to a subject-independent set to learn EEG motor imagery features. Firstly, a custom form of sequence inputs with spatial and temporal dimensions is adopted for dual headed attention via deep convolution net (DHDANet). Secondly, DHDANet simultaneously learns temporal and spacial features. The features of spacial attention on each input head are divided into two parts for spatial attentional learning subsequently. The proposed model is validated based on the EEG-MI signals collected from 54 subjects in two sessions with 200 trials in each sessions. The classification of left and right hand motor imagery in this paper achieves an average accuracy of 75.52%, a significant improvement compared to state-of-the-art methods. In addition, the visualization of the frequency analysis method demonstrates that the temporal-convolution and spectral-attention is capable of identifying the ERD for EEG-MI. The proposed machine learning structure enables cross-session and cross-subject classification and makes significant progress in the BMI transfer learning problem.

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Bilinear Regularized Locality Preserving Learning on Riemannian Graph for Motor Imagery BCI

Cited by 30 publications

References 36 publications

Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification

Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification

Multiple graph fusion based on Riemannian geometry for motor imagery classification

Dual Head and Dual Attention in Deep Learning for End-to-End EEG Motor Imagery Classification

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