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EEG Emotion Recognition Based on Graph Regularized Sparse Linear Regression

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Abstract

In this paper, a novel regression model, called graph regularized sparse linear regression (GRSLR), is proposed to deal with EEG emotion recognition problem. GRSLR extends the conventional linear regression method by imposing a graph regularization and a sparse regularization on the transform matrix of linear regression, such that it is able to simultaneously cope with sparse transform matrix learning while preserve the intrinsic manifold of the data samples. To detailed discuss the EEG emotion recognition, we collect a set of 14 subjects EEG emotion data and provide the experiment results on different features. To evaluate the proposed GRSLR model, we conduct experiments on the SEED database and RCLS database. The experimental results show that the proposed algorithm GRSLR is superior to the classic baselines. The RCLS database is made publicly available and other researchers could use it to test their own emotion recognition method.

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Acknowledgements

This work was supported by the National Basic Research Program of China under Grant 2015CB351704, the National Natural Science Foundation of China under Grant 61572009, Grant 61772276, and Grant 61602244, and the Jiangsu Provincial Key Research and Development Program under Grant BE2016616.

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Authors and Affiliations

  1. Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, 210096, Jiangsu, China

    Yang Li, Wenming Zheng, Yuan Zong & Sheng Ge

  2. School of Information Science and Engineering, Southeast University, Nanjing, 210096, Jiangsu, China

    Yang Li

  3. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210096, Jiangsu, China

    Zhen Cui

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  1. Yang Li
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  2. Wenming Zheng
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  3. Zhen Cui
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  4. Yuan Zong
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  5. Sheng Ge
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Correspondence to Wenming Zheng.

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Li, Y., Zheng, W., Cui, Z. et al. EEG Emotion Recognition Based on Graph Regularized Sparse Linear Regression. Neural Process Lett 49, 555–571 (2019). https://doi.org/10.1007/s11063-018-9829-1

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