Skip to main content
SpringerLink
Log in

Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm

  • Research Article
  • Published:
Cognitive Neurodynamics Aims and scope Submit manuscript

Abstract

Brain-computer interface (BCI) system based on motor imagery (MI) usually adopts multichannel Electroencephalograph (EEG) signal recording method. However, EEG signals recorded in multi-channel mode usually contain many redundant and artifact information. Therefore, selecting a few effective channels from whole channels may be a means to improve the performance of MI-based BCI systems. We proposed a channel evaluation parameter called position priori weight-permutation entropy (PPWPE), which include amplitude information and position information of a channel. According to the order of PPWPE values, we initially selected half of the channels with large PPWPE value from all sampling electrode channels. Then, the binary gravitational search algorithm (BGSA) was used in searching a channel combination that will be used in determining an optimal channel combination. The features were extracted by common spatial pattern (CSP) method from the final selected channels, and the classifier was trained by support vector machine. The PPWPE + BGSA + CSP channel selection method is validated on two data sets. Results showed that the PPWPE + BGSA + CSP method obtained better mean classification accuracy (88.0% vs. 57.5% for Data set 1 and 91.1% vs. 79.4% for Data set 2) than All-C + CSP method. The PPWPE + BGSA + CSP method can achieve higher classification in fewer channels selected. This method has great potential to improve the performance of MI-based BCI systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Acharya UR, Fujita H, Sudarshan VK, Bhat S, Koh JEW (2015) Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowl Based Syst 88:85–96

    Article  Google Scholar 

  • Alcaide-Aguirre RE, Huggins JE (2014) Novel hold-release functionality in a P300 brain–computer interface. J Neural Eng 11:066010

    Article  CAS  Google Scholar 

  • Alotaiby T, Abd El-Samie FE, Alshebeili SA, Ahmad I (2015) A review of channel selection algorithms for EEG signal processing. EURASIP J Adv Signal Process 201:66

    Article  Google Scholar 

  • Ang KK et al (2011) A large clinical study on the ability of stroke patients to use an EEG-based motor imagery brain–computer interface. Clin EEG Neurosci 42:253–258

    Article  Google Scholar 

  • Aydemir O, Ergun E (2019) A robust and subject-specific sequential forward search method for effective channel selection in brain computer interfaces. J Neurosci Methods 313:60–67

    Article  Google Scholar 

  • Bandt C, Pompe B (2002) Permutation entropy: a natural complexity measure for time series. Phys Rev Lett 88:174102

    Article  Google Scholar 

  • Blankertz B, Dornhege G, Krauledat M, Muller KR, Curio G (2007) The non-invasive Berlin brain–computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37:539–550

    Article  Google Scholar 

  • Chang CC, Lin CJ (2011) LIBSVM: a Library for Support Vector Machines. ACM Trans Intell Syst Technol 2:1–27

    Article  Google Scholar 

  • Cheng MM, Lu ZH, Wang HX (2017) Regularized common spatial patterns with subject-to-subject transfer of EEG signals. Cogn Neurodyn 11:173–181

    Article  Google Scholar 

  • Deng B, Cai LH, Li SN, Wang RF, Yu HT, Chen Y, Wang J (2017) Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer’s disease. Cogn Neurodyn 11:217–231

    Article  Google Scholar 

  • Dokare I, Kant N (2014) Classification of EEG signal for imagined left and right hand movement for brain computer interface applications. Int J Appl Innov Eng Manag 2014:291–294

    Google Scholar 

  • Dong EZ, Li CH, Li LT, Du SZ, Belkacem AN, Chen C (2017) Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain-computer interfaces. Med Biol Eng Comput 55:1809–1818

    Article  Google Scholar 

  • Dornhege G, Blankertz B, Curio G, Muller K-R (2004) Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms. IEEE Trans Biomed Eng 51:993–1002

    Article  Google Scholar 

  • Fadlallah B, Chen BD, Keil A, Principe J (2013) Weighted-permutation entropy: a complexity measure for time series incorporating amplitude information. Phys Rev E 87:022911

    Article  Google Scholar 

  • Feng JK et al (2018) Towards correlation-based time window selection method for motor imagery BCIs. Neural Netw 102:87–95

    Article  Google Scholar 

  • Gaume A, Dreyfus G, Vialatte FB (2019) A cognitive brain-computer interface monitoring sustained attentional variations during a continuous task. Cogn Neurodyn 13:257–269

    Article  Google Scholar 

  • Ghaemi A, Rashedi E, Pourrahimi AM, Kamandar M, Rahdari F (2017) Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm. Biomed Signal Process Control 33:109–118

    Article  Google Scholar 

  • Hazarika J (2019) Wavelet transform based approach for EEG feature selection of motor imagery data for brain computer interfaces. In: 3rd International conference on inventive systems and control (ICISC 2019)

  • He L, Hu Y, Li Y, Li D (2013) Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG. Neurocomputing 121:423–433

    Article  Google Scholar 

  • Jin J, Allison BZ, Sellers EW, Brunner C, Horki P, Wang XY, Neuper C (2011) An adaptive P300-based control system. J Neural Eng 8:036006

    Article  Google Scholar 

  • Jin J, Miao YY, Daly I, Zuo CL, Hu DW, Cichocki A (2019) Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Netw 118:262–270

    Article  Google Scholar 

  • Jing-Ru S, Jian-Guo W, Zhong-Tao X, Yuan Y, Junjiang L (2019) A method for EEG contributory channel selection based on deep belief network. In: 2019 IEEE 8th data driven control and learning systems conference (DDCLS) proceedings, pp 1247–1252

  • Kee C-Y, Ponnambalam SC, Loo C-K (2015) Multi-objective genetic algorithm as channel selection method for P300 and motor imagery data set. Neurocomputing 161:120–131

    Article  Google Scholar 

  • Kevric J, Subasi A (2017) Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. Biomed Signal Process Control 31:398–406

    Article  Google Scholar 

  • Kumar S, Sharma A (2018) A new parameter tuning approach for enhanced motor imagery EEG signal classification. Med Biol Eng Comput 56:1861–1874

    Article  Google Scholar 

  • Kumar S, Sharma A, Tsunoda T (2017) An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information. BMC Bioinform 18:545

    Article  Google Scholar 

  • McFarland DJ, Wolpaw JR (2011) Brain–computer interfaces for communication and control. Commun ACM 54:60–66

    Article  Google Scholar 

  • Miao MM, Wang AM, Liu FX (2017) A spatial-frequency-temporal optimized feature sparse representation-based classification method for motor imagery EEG pattern recognition. Med Biol Eng Comput 55:1589–1603

    Article  Google Scholar 

  • Nicolaou N, Georgiou J (2012) Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst Appl 39:202–209

    Article  Google Scholar 

  • Puanhvuan D, Khemmachotikun S, Wechakarn P, Wijarn B, Wongsawat Y (2017) Navigation-synchronized multimodal control wheelchair from brain to alternative assistive technologies for persons with severe disabilities. Cogn Neurodyn 11:117–134

    Article  Google Scholar 

  • Qiu ZY, Jin J, Lam HK, Zhang Y, Wang XY, Cichocki A (2016) Improved SFFS method for channel selection in motor imagery based BCI. Neurocomputing 207:519–527

    Article  Google Scholar 

  • Radman M, Chaibakhsh A, Nariman-zadeh N, Huiguang H (2019) Generalized sequential forward selection method for channel selection in EEG signals for classification of left or right hand movement in BCI. In: 2019 9th international conference on computer and knowledge engineering (ICCKE), pp 137–142

  • Raghu S, Sriraam N, Kumar GP (2017) Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier. Cogn Neurodyn 11:51–66

    Article  CAS  Google Scholar 

  • Ramoser H, Muller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 8:441–446

    Article  CAS  Google Scholar 

  • Rashedi E, Nezamabadi-pour H, Saryazdi S (2010) BGSA: binary gravitational search algorithm. Nat Comput 9:727–745

    Article  Google Scholar 

  • Selim S, Tantawi MM, Shedeed HA, Badr A (2018) A CSP\AM-BA-SVM approach for motor imagery BCI system. IEEE Access 6:49192–49208

    Article  Google Scholar 

  • Tam W-K, Ke Z, Tong K-Y (2011) Performance of common spatial pattern under a smaller set of EEG electrodes in brain-computer interface on chronic stroke patients: a multi-session dataset study. In: 2011 annual international conference of the IEEE engineering in medicine and biology society. IEEE, pp 6344–6347

  • Vapnik V, Vapnik V (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  • Yang JH, Singh H, Hines EL, Schlaghecken F, Iliescu DD, Leeson MS, Stocks NG (2012) Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach. Artif Intell Med 55:117–126

    Article  Google Scholar 

  • Zeng H, Yang C, Dai GJ, Qin FW, Zhang JH, Kong WZ (2018) EEG classification of driver mental states by deep learning. Cogn Neurodyn 12:597–606

    Article  Google Scholar 

  • Zhang Y, Wang Y, Jin J, Wang XY (2017) Sparse bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification. Int J Neural Syst 27:1650032

    Article  Google Scholar 

  • Zhao QB, Rutkowski TM, Zhang LQ, Cichocki A (2010) Generalized optimal spatial filtering using a kernel approach with application to EEG classification. Cogn Neurodynamics 4:355–358

    Article  Google Scholar 

  • Zuo CL et al (2019) Novel hybrid brain-computer interface system based on motor imagery and P300. Cogn Neurodyn 14:253–265

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key Research and Development Program 2017YFB13003002. This work was also supported in part by the Grant National Natural Science Foundation of China, under Grant Nos. 61573142, 61773164 and 91420302, the programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, and the “ShuGuang” project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation under Grant 19SG25.

Author information

Authors and Affiliations

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People’s Republic of China

    Hao Sun, Jing Jin, Cili Zuo, Shurui Li & Xingyu Wang

  2. School of Computer Science, Hangzhou Dianzi University, Hangzhou, China

    Wanzeng Kong

Authors
  1. Hao Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wanzeng Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cili Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shurui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xingyu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jing Jin or Wanzeng Kong.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, H., Jin, J., Kong, W. et al. Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm. Cogn Neurodyn 15, 141–156 (2021). https://doi.org/10.1007/s11571-020-09608-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11571-020-09608-3

Keywords

Search

Navigation