Electroencephalogram Clustering with Multiple Regularization Constrained Pseudo Label Propagation Optimization

Dai Chenglong; Li Guanghui; Li Dong; Shen Jiahua; Pi Dechang

doi:10.7544/issn1000-1239.202220295

Journal of Computer Research and Development > 2024 > 61(1): 156-171. > DOI: 10.7544/issn1000-1239.202220295

Dai Chenglong, Li Guanghui, Li Dong, Shen Jiahua, Pi Dechang. Electroencephalogram Clustering with Multiple Regularization Constrained Pseudo Label Propagation Optimization[J]. Journal of Computer Research and Development, 2024, 61(1): 156-171. DOI: 10.7544/issn1000-1239.202220295

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Electroencephalogram Clustering with Multiple Regularization Constrained Pseudo Label Propagation Optimization

1.
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122
2.
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106

Funds: This work was supported by the National Natural Science Foundation of China (62106087), the Natural Science Foundation of Jiangsu Province (BK20210455), and the Fundamental Research Funds for the Central Universities (JUSRP122033).

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Author Bio:
Dai Chenglong: born in 1988. PhD, lecturer. Member of CCF. His main research interests include brain-computer interface, EEG data mining, and machine learning

Li Guanghui: born in 1970. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include Internet of things, edge computing, fault tolerant computing, and nondestructive testing and evaluation

Li Dong: born in 1998. Master candidate. His main research interests include brain-computer interface and EEG analysis

Shen Jiahua: born in 1998. Master candidate. His main research interests include brain-computer interface and EEG analysis

Pi Dechang: born in 1971. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include big data analysis, data mining, and system reliability
Received Date: April 11, 2022
Revised Date: January 15, 2023
Available Online: June 25, 2023

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Abstract

Abstract

As the non-invasive analyzing media, electroencephalogram (EEG) signals are widely applied in brain-computer interfaces, dysfunctional disorder diagnosis and rehabilitation. However, the techniques used in such applications are supervised and completely require EEG labels, like classification. Meanwhile, with the ever-increasing of unlabeled EEG emerged in these applications, traditional supervised techniques are becoming inapplicable, which probably degrades the development of this new-type unlabeled EEG in the emerging potential fields. To deal with the issue of unsupervised analysis for unlabeled EEG signals, we propose a multiple regularization constrained pseudo label propagation optimization model, which integrates the pseudo label propagation learning, EEG similarity adjacency matrix approximation, and label classifier learning. Subsequently, to pursuit the goal of EEG clustering with the proposed model, we transform the model to a multi-objective optimization function and propose a gradient descent-based algorithm named EEGapc (electroencephalogram clustering with pseudo label propagation) to solve it. EEGapc not only can make best use of messages passing through pairwise EEG signals in EEG-constructed graph, but can also quickly converge to its local optima. Experimental results by comparing EEGapc with 8 different types of state-of-the-art clustering algorithms on 14 real-world EEG data sets clearly demonstrate the superiority of EEGapc, and its performances with respect to average NMI (normalized mutual information), ARI (adjusted rand index), F-score and kappa are at least improved by 86.88%, 58.01%, 6.29%, 61.17%, respectively.

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References (46)

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