Abstract
In the motor-imagery (MI) based brain computer interface (BCI), multi-channel electroencephalogram (EEG) is often used to ensure the complete capture of physiological phenomena. With the redundant information and noise, EEG signals cannot be easily converted into separable features through feature extraction algorithms. Channel selection algorithms are proposed to address the issue, in which the filtering technique is widely used with the advantages of low computational cost and strong practicability. In this study, we proposed several improved methods for filtering channel selection algorithm. Specifically, based on the coefficient of variation and inter-class distance, a novel channel classification method was designed, which divided channels into different categories based on their contribution to feature extraction process. Then a filtering channel selection algorithm was proposed according to the previous classification method. Moreover, a new testing framework for filtering channel selection algorithms was proposed, which can better reflect the generalization ability of the algorithm. Experimental results indicated that the proposed channel classification method is effective, and the proposed testing framework is better than the original one. Meanwhile, the proposed channel selection algorithm achieved the accuracy of 87.7% and 81.7% in two BCI competition datasets, respectively, which was superior to competing algorithms.
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References
Alotaiby T, El-Samie FEA, Alshebeili SA, Ahmad I (2015) A review of channel selection algorithms for EEG signal processing. EURASIP J Adv Sig Pr 2015:66. https://doi.org/10.1186/s13634-015-0251-9
Arvaneh M, Guan C, Ang KK, Quek C (2011) Optimizing the channel selection and classification accuracy in EEG-based BCI. IEEE Trans Biomed Eng 58:1865–1873. https://doi.org/10.1109/TBME.2011.2131142
Blankertz B, Dornhege G, Krauledat M, Müller K-R, Curio G (2007) The non-invasive Berlin brain–computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37:539–550. https://doi.org/10.1016/j.neuroimage.2007.01.051
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. https://doi.org/10.1023/A:1022627411411
Das A, Suresh S (2015) An effect-size based channel selection algorithm for mental task classification in brain computer interface. In: 2015 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 3140–3145. https://doi.org/10.1109/SMC.2015.545
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. https://doi.org/10.1109/TBME.2004.827088
Feng JK, Jin J, Daly I, Zhou J, Niu Y, Wang X, Cichocki A (2019) An optimized channel selection method based on multifrequency CSP-rank for motor imagery-based BCI system. Comput Intell Neurosci. https://doi.org/10.1155/2019/8068357
Guarnieri R, Zhao M, Taberna GA, Ganzetti M, Swinnen SP, Mantini D (2021) RT-NET: real-time reconstruction of neural activity using high-density electroencephalography. Neuroinformatics 19:251–266. https://doi.org/10.1007/s12021-020-09479-3
Gwin JT, Gramann K, Makeig S, Ferris DP (2010) Removal of movement artifact from high-density EEG recorded during walking and running. J Neurophysiol 103:3526–3534. https://doi.org/10.1152/jn.00105.2010
Hastie T, Tibshirani RJ, J.H F (2009) The elements of statistical learning: Springer. Elements 1:267–268. https://doi.org/10.1198/tech.2003.s770
Hsu C-W, Chang C-C, Lin C-J (2003) A practical guide to support vector classification. Tech. rep., Department of Computer Science, National Taiwan University
Huang M, Jin J, Zhang Y, Hu D, Wang X (2018) Usage of drip drops as stimuli in an auditory P300 BCI paradigm. Cogn Neurodyn 12:85–94. https://doi.org/10.1007/s11571-017-9456-y
Jiao Y, Zhou T, Yao L, Zhou G, Wang X, Zhang Y (2020) Multi-view multi-scale optimization of feature representation for EEG classification improvement. IEEE Trans Neural Syst Rehab Eng 28:2589–2597. https://doi.org/10.1109/TNSRE.2020.3040984
Jin J, Horki P, Brunner C, Wang X, Neuper C, Pfurtscheller G (2010) A new P300 stimulus presentation pattern for EEG-based spelling systems. Biomed Tech/bIomed Eng 55:203–210. https://doi.org/10.1515/BMT.2010.029
Jin J, Li S, Daly I, Miao Y, Liu C, Wang X, Cichocki A (2020a) The study of generic model set for reducing calibration time in P300-based brain–computer interface. IEEE Trans Neural Syst Rehab Eng 28:3–12. https://doi.org/10.1109/TNSRE.2019.2956488
Jin J, Liu C, Daly I, Miao Y, Li S, Wang X, Cichocki A (2020b) Bispectrum-based channel selection for motor imagery based brain–computer interfacing. IEEE Trans Neural Syst Rehab Eng 28:2153–2163. https://doi.org/10.1109/TNSRE.2020.3020975
Lal TN, Schroder M, Hinterberger T, Weston J, Bogdan M, Birbaumer N, Scholkopf B (2004) Support vector channel selection in BCI. IEEE Trans Biomed Eng 51:1003–1010. https://doi.org/10.1109/TBME.2004.827827
Lan T, Erdogmus D, Adami A, Mathan S, Pavel M (2007) Channel selection and feature projection for cognitive load estimation using ambulatory EEG. Comput Intell Neurosci. https://doi.org/10.1155/2007/74895
Leeb R, Friedman D, Muller-Putz GR, Scherer R, Slater M, Pfurtscheller G (2007) Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput Intell Neurosci. https://doi.org/10.1155/2007/79642
Liu Q, Jiao Y, Miao Y, Zuo C, Wang X, Cichocki A, Jin J (2020) Efficient representations of EEG signals for SSVEP frequency recognition based on deep multiset CCA. Neurocomputing 378:36–44. https://doi.org/10.1016/j.neucom.2019.10.049
Meng J, Liu G, Huang G, Zhu X Automated selecting subset of channels based on CSP in motor imagery brain–computer interface system. In: Robotics and biomimetics, 2009. pp 2290–2294. https://doi.org/10.1109/ROBIO.2009.5420462
Meng L, Jin J, Wang X A comparison of three electrode channels selection methods applied to SSVEP BCI. In: 2011 4th international conference on biomedical engineering and informatics (BMEI), 2011. IEEE, pp 584–587. https://doi.org/10.1109/BMEI.2011.6098285
Miao Y, Yin E, Allison BZ, Zhang Y, Jin J (2019) An ERP-based BCI with peripheral stimuli: validation with ALS patients. Cogn Neurodyn 14:21–33. https://doi.org/10.1007/s11571-019-09541-0
Pfurtscheller G (1977) Graphical display and statistical evaluation of event-related desynchronization (ERD). Electroencephalogr Clin Neurophysiol 43:757–760. https://doi.org/10.1016/0013-4694(77)90092-X
Pfurtscheller G (1992) Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest. Electroencephalogr Clin Neurophysiol 83:62–69. https://doi.org/10.1016/0013-4694(92)90133-3
Pfurtscheller G, Brunner C, Schlögl A, Lopes da Silva FH (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31:153–159. https://doi.org/10.1016/j.neuroimage.2005.12.003
Qiu Z, Jin J, Lam H-K, Zhang Y, Wang X, Cichocki A (2016) Improved SFFS method for channel selection in motor imagery based BCI. Neurocomputing 207:519–527. https://doi.org/10.1016/j.neucom.2016.05.035
Shi B, Wang Q, Yin S, Yue Z, Huai Y, Wang J (2021) A binary harmony search algorithm as channel selection method for motor imagery-based BCI. Neurocomputing 443:12–25. https://doi.org/10.1016/j.neucom.2021.02.051
Sun H, Jin J, Kong W, Zuo C, Wang X (2020) Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm. Cogn Neurodyn 15:141–156. https://doi.org/10.1007/s11571-020-09608-3
Tam W-K, Ke Z, Tong K-Y 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, 2011. IEEE, pp 6344–6347.
Varsehi H, Firoozabadi SMP (2021) An EEG channel selection method for motor imagery based brain–computer interface and neurofeedback using Granger causality. Neural Netw 133:193–206. https://doi.org/10.1016/j.neunet.2020.11.002
Wang Y, Gao S, Gao X (2006) Common spatial pattern method for channel selelction in motor imagery based brain–computer interface. In: 27th annual conference in engineering in medicine and biology, 2006. IEEE, pp 5392–5395.
Wierzgała P, Zapała D, Wojcik GM, Masiak J (2018) Most popular signal processing methods in motor-imagery BCI: a review and meta-analysis. Front Neuroinform 12:78. https://doi.org/10.3389/fninf.2018.00078
Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol 113:767–791. https://doi.org/10.1016/S1388-2457(02)00057-3
Zuo C, Jin J, Yin E, Saab R, Cichocki A (2019) Novel hybrid brain–computer interface system based on motor imagery and P300. Cogn Neurodyn 14:253–265. https://doi.org/10.1007/s11571-019-09560-x
Acknowledgements
The authors are grateful for the support of the National Key Research and Development Program (2017YFB13003002), the Grant National Natural Science Foundation of China (61573142, 61773164, and 91420302), and the program of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.
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Xiao, R., Huang, Y., Xu, R. et al. Coefficient-of-variation-based channel selection with a new testing framework for MI-based BCI. Cogn Neurodyn 16, 791–803 (2022). https://doi.org/10.1007/s11571-021-09752-4
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DOI: https://doi.org/10.1007/s11571-021-09752-4