EEG Mental Recognition Based on RKHS Learning and Source Dictionary Regularized RKHS Subspace Learning

Lei, Wenjie; Ma, Zhengming; Liu, Shu-Yu; Lin, Yuanping

doi:10.1109/access.2021.3124028

Cited by 4 publications

(4 citation statements)

References 36 publications

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“…When the dimensionality of the original EEG data is high, the data computational and storage costs will be very large. Thus, a common solution is to project the high-dimensional data into a low-dimensional space (Lei et al, 2021). Let Q ∈ R d×C be the projection matrix, the projection data can be represented as…”

Section: Subspace Learningmentioning

confidence: 99%

“…Therefore, it is still a challenging task to use machine learning methods to identify AD based on EEG signals. To solve this problem, the researchers usually reduce the dimension of EEG high-dimensional data and extract a small amount of the most valuable compact information, which not only saves storage space and processing time but also enables learning a robust model (Lei et al, 2021). Subspace learning and low-rank representation can well achieve this goal.…”

Section: Introductionmentioning

confidence: 99%

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Discriminant Subspace Low-Rank Representation Algorithm for Electroencephalography-Based Alzheimer’s Disease Recognition

Tang

Li²,

Zhou³

et al. 2022

Front. Aging Neurosci.

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Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that often occurs in the elderly. Electroencephalography (EEG) signals have a strong correlation with neuropsychological test results and brain structural changes. It has become an effective aid in the early diagnosis of AD by exploiting abnormal brain activity. Because the original EEG has the characteristics of weak amplitude, strong background noise and randomness, the research on intelligent AD recognition based on machine learning is still in the exploratory stage. This paper proposes the discriminant subspace low-rank representation (DSLRR) algorithm for EEG-based AD and mild cognitive impairment (MCI) recognition. The subspace learning and low-rank representation are flexibly integrated into a feature representation model. On the one hand, based on the low-rank representation, the graph discriminant embedding is introduced to constrain the representation coefficients, so that the robust representation coefficients can preserve the local manifold structure of the EEG data. On the other hand, the least squares regression, principle component analysis, and global graph embedding are introduced into the subspace learning, to make the model more discriminative. The objective function of DSLRR is solved by the inexact augmented Lagrange multiplier method. The experimental results show that the DSLRR algorithm has good classification performance, which is helpful for in-depth research on AD and MCI recognition.

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Section: Subspace Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discriminant Subspace Low-Rank Representation Algorithm for Electroencephalography-Based Alzheimer’s Disease Recognition

Tang

Li²,

Zhou³

et al. 2022

Front. Aging Neurosci.

View full text Add to dashboard Cite

show abstract

“…This property was leveraged to embed different type of data (Harandi et al, 2014;Brooks et al, 2019b). This motivated the development of different machine learning algorithms (Chevallier et al, 2017;Yair et al, 2019;Zhuang et al, 2020;Lei et al, 2021;Ju and Guan, 2022) and of neural networks architectures (Huang and Van Gool, 2017;Brooks et al, 2019a).…”

Section: Symmetric Positive Definite Matricesmentioning

confidence: 99%

Subspace Detours Meet Gromov–Wasserstein

et al. 2021

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In the context of optimal transport (OT) methods, the subspace detour approach was recently proposed by Muzellec and Cuturi. It consists of first finding an optimal plan between the measures projected on a wisely chosen subspace and then completing it in a nearly optimal transport plan on the whole space. The contribution of this paper is to extend this category of methods to the Gromov–Wasserstein problem, which is a particular type of OT distance involving the specific geometry of each distribution. After deriving the associated formalism and properties, we give an experimental illustration on a shape matching problem. We also discuss a specific cost for which we can show connections with the Knothe–Rosenblatt rearrangement.

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“…How to use label information is always the focus of various domain adaptation algorithms. Lei et al [ 27 ] applied the dictionary learning to the source domain while we borrowed the idea of LDA.…”

Section: Introductionmentioning

confidence: 99%

Regularized RKHS-Based Subspace Learning for Motor Imagery Classification

Jiang

Liu

et al. 2022

Entropy

Self Cite

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Brain–computer interface (BCI) technology allows people with disabilities to communicate with the physical environment. One of the most promising signals is the non-invasive electroencephalogram (EEG) signal. However, due to the non-stationary nature of EEGs, a subject’s signal may change over time, which poses a challenge for models that work across time. Recently, domain adaptive learning (DAL) has shown its superior performance in various classification tasks. In this paper, we propose a regularized reproducing kernel Hilbert space (RKHS) subspace learning algorithm with K-nearest neighbors (KNNs) as a classifier for the task of motion imagery signal classification. First, we reformulate the framework of RKHS subspace learning with a rigorous mathematical inference. Secondly, since the commonly used maximum mean difference (MMD) criterion measures the distribution variance based on the mean value only and ignores the local information of the distribution, a regularization term of source domain linear discriminant analysis (SLDA) is proposed for the first time, which reduces the variance of similar data and increases the variance of dissimilar data to optimize the distribution of source domain data. Finally, the RKHS subspace framework was constructed sparsely considering the sensitivity of the BCI data. We test the proposed algorithm in this paper, first on four standard datasets, and the experimental results show that the other baseline algorithms improve the average accuracy by 2–9% after adding SLDA. In the motion imagery classification experiments, the average accuracy of our algorithm is 3% higher than the other algorithms, demonstrating the adaptability and effectiveness of the proposed algorithm.

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EEG Mental Recognition Based on RKHS Learning and Source Dictionary Regularized RKHS Subspace Learning

Cited by 4 publications

References 36 publications

Discriminant Subspace Low-Rank Representation Algorithm for Electroencephalography-Based Alzheimer’s Disease Recognition

Discriminant Subspace Low-Rank Representation Algorithm for Electroencephalography-Based Alzheimer’s Disease Recognition

Subspace Detours Meet Gromov–Wasserstein

Regularized RKHS-Based Subspace Learning for Motor Imagery Classification

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