Task-free brainprint recognition based on low-rank and sparse decomposition model

Kong, Wanzeng; Kong, Xianghao; Fan, Qiaonan; Zhao, Qibin; Cichocki, Andrzej

doi:10.1504/ijdmb.2019.100629

Cited by 5 publications

(3 citation statements)

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“…Compared with our reconstructed despiked electroencephalogram, it can be concluded that our proposed method outperforms the low-rank decomposition-based method in terms of retaining the characteristics of the electroencephalogram. Finally, the low-rank decomposition-based method [18][19][20][21][22] is also compared. Here, the low-rank component obtained by applying the low-rank decomposition to the filtered electroencephalogram is taken as the despiked electroencephalogram.…”

Section: Comparisons To the Existing Methodsmentioning

confidence: 99%

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Suppressing the Spikes in Electroencephalogram via an Iterative Joint Singular Spectrum Analysis and Low-Rank Decomposition Approach

Tian

Ling

Zhou

et al. 2020

Sensors

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The novelty and the contribution of this paper consists of applying an iterative joint singular spectrum analysis and low-rank decomposition approach for suppressing the spikes in an electroencephalogram. First, an electroencephalogram is filtered by an ideal lowpass filter via removing its discrete Fourier transform coefficients outside the δ wave band, the θ wave band, the α wave band, the β wave band and the γ wave band. Second, the singular spectrum analysis is performed on the filtered electroencephalogram to obtain the singular spectrum analysis components. The singular spectrum analysis components are sorted according to the magnitudes of their corresponding eigenvalues. The singular spectrum analysis components are sequentially added together starting from the last singular spectrum analysis component. If the variance of the summed singular spectrum analysis component under the unit energy normalization is larger than a threshold value, then the summation is terminated. The summed singular spectrum analysis component forms the first scale of the electroencephalogram. The rest singular spectrum analysis components are also summed up together separately to form the residue of the electroencephalogram. Next, the low-rank decomposition is performed on the residue of the electroencephalogram to obtain both the low-rank component and the sparse component. The low-rank component is added to the previous scale of the electroencephalogram to obtain the next scale of the electroencephalogram. Finally, the above procedures are repeated on the sparse component until the variance of the current scale of the electroencephalogram under the unit energy normalization is larger than another threshold value. The computer numerical simulation results show that the spike suppression performance based on our proposed method outperforms that based on the state-of-the-art methods.

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Section: Comparisons To the Existing Methodsmentioning

confidence: 99%

“…Because of this reason, the bandpass filtering is required to obtain the signal bands of the electroencephalogram. [18][19][20][21][22] Let an observed signal y(n) be a combination of two components, namely the signal component s(n) and the noise component e(n). That is, y(n) = s(n) + e(n).…”

Section: Reviews On the Singular Spectrum Analysis And The Low-rank Dmentioning

confidence: 99%

Suppressing the Spikes in Electroencephalogram via an Iterative Joint Singular Spectrum Analysis and Low-Rank Decomposition Approach

Tian

Ling

Zhou

et al. 2020

Sensors

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“…7.5 Hz square SSVEP with targeted and non-targeted Snodgrass-Vanderwart's set of images were utilized as ERP stimulus for 20 healthy subjects, and Long Short-Term Memory (LSTM) network was used for brainprint recognition, the accuracy rate reached 91.44%, which confirmed the feasibility of dual-task brainprint recognition. In 2019, Kong et al [30] proposed an Low-Rank Matrix Decomposition (LRMD)-based background EEG (BEEG)extracted algorithm that combined maximum correntropy criterion (MCC) with the rational quadratic kernel, which can effectively extract BEEG and verified it on four datasets. The high classification accuracy proved that multi-task brainprint recognition is feasible.…”

Section: A Brainprint Recognition With Specific Taskmentioning

confidence: 99%

CTNN: A Convolutional Tensor-Train Neural Network for Multi-Task Brainprint Recognition

Jin

Tang

Kong

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Brainprint is a new type of biometric in the form of EEG, directly linking to intrinsic identity. Currently, most methods for brainprint recognition are based on traditional machine learning and only focus on a single brain cognition task. Due to the ability to extract high-level features and latent dependencies, deep learning can effectively overcome the limitation of specific tasks, but numerous samples are required for model training. Therefore, brainprint recognition in realistic scenes with multiple individuals and small amounts of samples in each class is challenging for deep learning. This article proposes a Convolutional Tensor-Train Neural Network (CTNN) for the multi-task brainprint recognition with small number of training samples. Firstly, local temporal and spatial features of the brainprint are extracted by the convolutional neural network (CNN) with depthwise separable convolution mechanism. Afterwards, we implement the TensorNet (TN) via low-rank representation to capture the multilinear intercorrelations, which integrates the local information into a global one with very limited parameters. The experimental results indicate that CTNN has high recognition accuracy over 99% on all four datasets, and it exploits brainprint under multi-task efficiently and scales well on training samples. Additionally, our method can also provide an interpretable biomarker, which shows specific seven channels are dominated for the recognition tasks.

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Rational-quadratic kernel-based maximum correntropy Kalman filter for the non-Gaussian noises

Zhao,

Mu,

Yang

et al. 2024

Journal of the Franklin Institute

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Task-free brainprint recognition based on low-rank and sparse decomposition model

Cited by 5 publications

References 0 publications

Suppressing the Spikes in Electroencephalogram via an Iterative Joint Singular Spectrum Analysis and Low-Rank Decomposition Approach

Suppressing the Spikes in Electroencephalogram via an Iterative Joint Singular Spectrum Analysis and Low-Rank Decomposition Approach

CTNN: A Convolutional Tensor-Train Neural Network for Multi-Task Brainprint Recognition

Rational-quadratic kernel-based maximum correntropy Kalman filter for the non-Gaussian noises

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