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Balanced Graph-based regularized semi-supervised extreme learning machine for EEG classification

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Abstract

Machine learning algorithms play a critical role in electroencephalograpy (EEG)-based brain-computer interface (BCI) systems. However, collecting labeled samples for classifier training and calibration is still difficult and time-consuming, especially for patients. As a promising alternative way to address the problem, semi-supervised learning has attracted much attention by exploiting both labeled and unlabeled samples in the training process. Nowadays, semi-supervised extreme learning machine (SS-ELM) is widely used in EEG classification due to its fast training speed and good generalization performance. However, the classification performance of SS-ELM largely depends on the quality of sample graph. The graphs of most semi-supervised algorithms are constructed by the similarity between labeled and unlabeled data called manifold graph. The more similar the structural information between samples, the greater probability they belong to the same class. In this paper, the label-consistency graph (LCG) and sample-similarity graph (SSG) are combined to constrain the model output. When the SSG is not accurate enough, the weight of LCG needs to be increased, and vice versa. The weight ratio of two graphs is optimized to obtain an optimal adjacency graph, and finally the best output weight vector is achieved. To verify the effectiveness of the proposed algorithm, it was validated and compared with several existing methods on two real datasets: BCI Competition IV Dataset 2a and BCI Competition III Dataset 4a. Experimental results show that our algorithm has achieved the promising results, especially when the number of labeled samples is small.

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Acknowledgements

This work was supported by National Natural Science Foundation of China under Grant Nos. 61871427 and 61971168, Key Research & Development Project of Zhejiang Province (2020C04009) and Graduate Education & Teaching Reform Project of Hangzhou Dianzi University (No. JXGG2019ZD001).

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Authors and Affiliations

  1. School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China

    Qingshan She, Jie Zou, Ming Meng, Yingle Fan & Zhizeng Luo

  2. Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, 310018, Zhejiang, China

    Qingshan She

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  1. Qingshan She
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Correspondence to Qingshan She.

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She, Q., Zou, J., Meng, M. et al. Balanced Graph-based regularized semi-supervised extreme learning machine for EEG classification. Int. J. Mach. Learn. & Cyber. 12, 903–916 (2021). https://doi.org/10.1007/s13042-020-01209-0

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