Abstract
Brain connectivity analysis via complex networks has been widely applied to elucidate functional aspects related to brain diseases, such as Alzheimer and Parkinson, and, more recently, to investigations concerning the functional organization of brain regions under motor imagery in brain computer interfaces (BCIs). Therefore, this work seeks to investigate the classification performance of steady-state visually evoked potential (SSVEP) brain-computer interfaces based on functional connectivity. Two different approaches were chosen for extracting functional connectivity and estimating the adjacency matrix from SSVEP-EEG signals: classical Pearson correlation and a new proposal based on Space-Time recurrence counting. These strategies were followed by graph feature evaluation (clustering coefficient, degree, betweenness and eigenvalue centralities), feature selection via Davies-Bouldin index and classification using a least squares classifier for 15 subjects in a 4-command SSVEP-BCI system. For comparison, we also employed a classical spectral feature extraction approach based on the fast Fourier transform (FFT). It was observed that it is possible to separate the classes with a mean accuracy of 0.56 for Pearson and 0.61 for the STR framework, with the clustering coefficient and the eigenvector centrality being the best attributes for these scenarios, respectively. Nonetheless, classical FFT-based feature extraction obtained the best decoding performance.
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References
Sporns, O.: Networks of the Brain. The MIT Press (2011)
Jie, B., Wee, C.Y., Shen, D., Zhang, D.: Hyper-connectivity of functional networks for brain disease diagnosis. Med. Image Anal. 32, 84–100 (2016)
Friston, J.: Functional and effective connectivity: a review. Brain Connectivity 1(1), 13–36 (2011)
Wolpaw, J., Wolpaw, E.W.: Brain-Computer Interfaces: Principles and Practice. Oxford University Press (2012)
Carvalho, S.N., Costa, T.B.S., Uribe, L.F.S., Soriano, D.C., Yared, G.F.G., Coradine, L.C., Attux, R.: Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs. Biomed. Signal Process. Control 21, 34–42 (2015)
Uribe, L.F.S., Fazanaro, F.I., Castellano, G., Suyama, R., Attux, R., Cardozo, E., Soriano, D.C.: A recurrence-based approach for feature extraction in brain-computer interface systems. In: Marwan, N., Riley, M., Giuliani, A., Webber Jr., C. (eds.) Translational Recurrences. Springer Proceedings in Mathematics & Statistics, vol. 103, pp. 95–107. Springer (2014)
Hamedi, M., Salleh, S., Noor, A.M.: Electroencephalographic motor imagery brain connectivity analysis for BCI: a review. Neural Comput. 28(6), 999–1041 (2016)
Rodrigues, P.G.: Extração de características em interfaces cérebro-máquina utilizando métricas de redes complexas. Dissertação de Mestrado. Universidade Federal do ABC (2018)
Guo, M., Xu, G., Wang, L., Fu, L.: Functional brain network analysis during auditory oddball task. In: Asia-Pacific International Symposium on Electromagnetic Compatibility (APEMC), pp. 1098–1100 (2016)
Kabbara, A., Khalil, M., El-Falou, W., Eid, H., Hassan, M.: Functional brain connectivity as a new feature for P300 speller. PLoS ONE 11(1), e0146282 (2016)
Zhang, Y., Xu, P., Huang, Y., Cheng, K., Yao, D.: SSVEP response is related to functional brain network topology entrained by the flickering stimulus. PLoS ONE 8(9), e72654 (2013)
Zhang, Y., Xu, P., Guo, D., Yao, D.: Prediction of SSVEP-based BCI performance by the resting-state EEG network. J. Neural Eng. 10, 66017 (2013)
Ghosh, P., Mazumder, A., Bhattacharyya, S., Tibarewala, D.N., Hayashibe, M.: Functional connectivity analysis of motor imagery EEG signal for brain-computer interfacing application. In: 7th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 210–213. IEEE, Montpellier, France (2015)
Stefano Filho, C.A., Attux, R., Castellano, G.: Can graph metrics be used for EEG-BCIs based on hand motor imagery? Biomed. Signal Process. Control 40, 359–365 (2018)
Jalili, M., Knyazeva, M.G.: EEG-based functional networks in schizophrenia. Comput. Biol. Med. 41(12), 1178–1186 (2011)
Marwan, N., Carmen Romano, M., Thiel, M., Kurths, J.: Recurrence plots for the analysis of complex systems. Phys. Rep. 438(5–6), 237–329 (2007)
Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretation. Neuroimage 52(3), 1059–1069 (2010)
Lohmann, G., Margulies, D.S., Horstmann, A., Pleger, B., Lepsien, J., Goldhahn, D., Schloegl, H., Stumvoll, M., Villringer, A., Turner, R.: Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain. PLoS ONE 5(4), e10232 (2010)
Yan, B., Hongwei, L., Li, Z., Genghuang, Y., Liqing, G.: Research on steady state visual evoked potentials based on wavelet packet technology for brain-computer interface. Procedia Eng. 15, 2629–2633 (2011)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1(2), 224–227 (1979)
Uribe, L.F.S., Cardozo, E., Attux, R., Soriano, D.C.: An implementation of SSVEP-BCI system based on a cluster measure for feature selection. In: IEEE Biosignals and Biorobotics Conference, pp. 1–6. IEEE, Salvador, Brazil (2014)
Acknowledgements
Financial support from FAPESP (n. 2015/24260-7 and 2013/07559-3), CNPq (n. 449467/2014-7, 305621/2015-7 and 305616/2016-1) and FINEP (n. 01.16.0067.00)
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Rodrigues, P.G., Silva Júnior, J.I., Costa, T.B.S., Attux, R., Castellano, G., Soriano, D.C. (2019). Classification Performance of SSVEP Brain-Computer Interfaces Based on Functional Connectivity. In: Costa-Felix, R., Machado, J., Alvarenga, A. (eds) XXVI Brazilian Congress on Biomedical Engineering. IFMBE Proceedings, vol 70/2. Springer, Singapore. https://doi.org/10.1007/978-981-13-2517-5_18
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DOI: https://doi.org/10.1007/978-981-13-2517-5_18
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