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Intentions Recognition of EEG Signals with High Arousal Degree for Complex Task

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

This paper presents a novel electroencephalography (EEG) evoked paradigm based on neurological rehabilitation. By implementing a conceptual model “cup-and-ball” system, EEG signals in manipulating the dynamic constrained objects are generated. Based on the operational EEG signals, a method is proposed to recognize different mental intentions. Under the manipulating task with a high arousal level, common spatial patterns (CSP) is used to extract and optimize features of the EEG signals from ten participants. Quadratic discriminant analysis (QDA) is implemented on EEG signals in different dimensions to identify different EEG patterns. The cross-validation is used to make classifier adaptive to a given data set. The receiver operating characteristic (ROC) curves are presented to illustrate recognition performance. The classification effect of QDA is verified by paired t-test (P < 0.001). Based on the proposed method, the average accuracy of mental intentions is 0.9857 ± 0.0191 and the area under the ROC curve (AUC) is 0.9665 ± 0.0291. The performance of QDA is also compared with the other three classifiers such as the support vector machine (SVM), the decision tree (DT) and the k-nearest neighborhood (k-NN) rule. The results suggest that the proposed method is very competitive with other methods.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 51605419, 61973262), Natural Science Foundation of Hebei Province (Grant No. E2018203433), China Postdoctoral Science Foundation (Grant No. 2016 M600193), Hebei Province Funding Project for Returned Overseas Scholar (Grant No. CL201727). We gratefully acknowledge the help of Professor Deniz Erdogmus and Doctor Shalini Puwar in the dynamic behavior modeling. Thanks for all participants of experiments.

Funding

This work was funded by the National Natural Science Foundation of China (Grant No. 51605419, 61,973,262), Natural Science Foundation of Hebei Province (Grant No. E2018203433), China Postdoctoral Science Foundation (Grant No. 2016 M600193), Hebei Province Funding Project for Returned Overseas Scholar (Grant No. CL201727).

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Authors and Affiliations

  1. Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

    Rongrong Fu, Mengmeng Han & Peiming Shi

  2. School of Mechanical Engineering, Northeast Electric Power University, Jilin, 132012, China

    Fuwang Wang

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  1. Rongrong Fu
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  2. Mengmeng Han
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  3. Fuwang Wang
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  4. Peiming Shi
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Correspondence to Rongrong Fu.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

Clarification and Statement

This manuscript by Rongrong Fu, Mengmeng Han, Fuwang Wang, Peiming Shi titled “Intentions Recognition of EEG Signals with High Arousal Degree for Complex Task” is an original unpublished work and the manuscript or any variation of it has not been submitted to any other publication previously. All of the authors have agreed with the submission.

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This article is part of the Topical Collection on Image & Signal Processing

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Fu, R., Han, M., Wang, F. et al. Intentions Recognition of EEG Signals with High Arousal Degree for Complex Task. J Med Syst 44, 110 (2020). https://doi.org/10.1007/s10916-020-01571-0

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