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Motor Imagery EEG Classification Based on Kernel Hierarchical Extreme Learning Machine

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Abstract

As connections from the brain to an external device, Brain-Computer Interface (BCI) systems are a crucial aspect of assisted communication and control. When equipped with well-designed feature extraction and classification approaches, information can be accurately acquired from the brain using such systems. The Hierarchical Extreme Learning Machine (HELM) has been developed as an effective and accurate classification approach due to its deep structure and extreme learning mechanism. A classification system for motor imagery EEG signals is proposed based on the HELM combined with a kernel, herein called the Kernel Hierarchical Extreme Learning Machine (KHELM). Principle Component Analysis (PCA) is used to reduce the dimensionality of the data, and Linear Discriminant Analysis (LDA) is introduced to push the features away from different classes. To demonstrate the performance, the proposed system is applied to the BCI competition 2003 Dataset Ia, and the results are compared with those from state-of-the-art methods; we find that the accuracy is up to 94.54%.

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Acknowledgements

This research is partially sponsored by the Natural Science Foundation of China (Nos. 61672070, 81471770, 61572004 and 61650201), the Beijing Municipal Natural Science Foundation (4152005, 4162058), the Science and Technology Program of Tianjin (15YFXQGX0050), and the Qinghai Natural Science Foundation (2016-ZJ-Y04).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Lijuan Duan, Menghu Bao & Song Cui

  2. Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, Beijing, China

    Lijuan Duan

  3. National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing, 100124, China

    Menghu Bao & Song Cui

  4. College of Applied Science, Beijing University of Technology, Beijing, 100124, China

    Yuanhua Qiao

  5. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research and School of Computer Science, Beijing Information Science and Technology University, Beijing, 100101, China

    Jun Miao

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  1. Lijuan Duan
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  2. Menghu Bao
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  4. Yuanhua Qiao
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  5. Jun Miao
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Correspondence to Jun Miao.

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Duan, L., Bao, M., Cui, S. et al. Motor Imagery EEG Classification Based on Kernel Hierarchical Extreme Learning Machine. Cogn Comput 9, 758–765 (2017). https://doi.org/10.1007/s12559-017-9494-0

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  • DOI: https://doi.org/10.1007/s12559-017-9494-0

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