Abstract
The evaluation of the data/information fusion systems does not have standard quality criteria making the reuse and optimization of these systems a complex task. In this work, we propose a complete low data fusion (DF) framework based on the Joint Director of Laboratories (JDL) model, which considers contextual information alongside information quality (IQ) and performance evaluation system to optimize the DF process according to the user requirements. A set of IQ criteria was proposed by level. The model was tested with a brain-computer interface (BCI) system multi-environment to prove its functionality. The first level makes the selection and preprocessing of electroencephalographic signals. In level one feature extraction is carried out using discrete wavelet transform (DWT), nonlinear and linear statistical measures, and Fuzzy Rough Set – FRS algorithm for selecting the relevant features; finally, in the same level a classification process was conducted using support vector machine – SVM. A Fuzzy Inference system is used for controlling different processes based on the results given by an IQ evaluation system, which applies quality measures that can be weighted by the users of the system according to their requirements. Besides, the system is optimized based on the results given by the cuckoo search algorithm, which uses the IQ traceability for maximizing the IQ criteria according to user requirements. The test was carried out with different type and levels of noise applied to the signals. The results showed the capability and functionality of the model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Leeb, R., Sagha, H., Chavarriaga, R., del R Millán, J.: A hybrid brain–computer interface based on the fusion of electroencephalographic and electromyographic activities. J. Neural Eng. 8(2), 025011 (2011)
Oken, B., Orhan, U., Roark, B., Erdogmus, D., Fowler, A., Mooney, A., Peters, B., Miller, M., Fried-Oken, M.B.: Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome. Neurorehabil. Neural Repair 28(4), 387–394 (2014)
Smith, D., Singh, S.: Approaches to multisensor data fusion in target tracking: a survey. IEEE Trans. Knowl. Data Eng. 18(12), 1696–1710 (2006)
Ardeshir Goshtasby, A., Nikolov, S.: Image fusion: advances in the state of the art. Inf. Fusion 8(2), 114–118 (2007)
Hadzagic, M., Valin, P., Shahbazian, E.: Reliability and relevance in the Thresholded Dempster-Shafer Algorithm for ESM data fusion. In: Information Fusion (FUSION), pp. 615–620 (2012)
Khaleghi, B., Khamis, A., Karray, F.O., Razavi, S.N.: Multisensor data fusion: a review of the state-of-the-art. Inf. Fusion 14(1), 28–44 (2013)
Esteban, J., Starr, A., Willetts, R., Hannah, P., Bryanston-Cross, P.: A review of data fusion models and architectures: towards engineering guidelines. Neural Comput. Appl. 14(4), 273–281 (2005)
Sidek, O., Quadri, S.A.: A review of data fusion models and systems. Int. J. Image Data Fusion 3(1), 3–21 (2012)
Rogova, G.L., Bosse, E.: Information quality in information fusion. In: 2010 13th International Conference on Information Fusion, pp. 1–8 (2010)
Lahat, D., Adaly, T., Jutten, C.: Challenges in multimodal data fusion. In: 2014 Proceedings of the 22nd European Signal Processing Conference (EUSIPCO), pp. 101–105 (2014)
van Laere, J.: Challenges for IF performance evaluation in practice. In: 2009 12th International Conference on Information Fusion, FUSION 2009, pp. 866–873 (2009)
Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. J. Manag. Inf. Syst. 12(4), 5–33 (1996)
Lee, Y.W., Strong, D.M., Kahn, B.K., Wang, R.Y.: AIMQ: a methodology for information quality assessment. Inf. Manag. 40(2), 133–146 (2002)
Stvilia, B., Gasser, L., Twidale, M.B., Smith, L.C.: A framework for information quality assessment. J. Am. Soc. Inf. Sci. Technol. 58(12), 1720–1733 (2007)
Blasch, E., Valin, P., Bosse, E.: Measures of effectiveness for high-level fusion. In: 2010 13th International Conference on Information Fusion, pp. 1–8 (2010)
Todoran, I., Lecornu, L., Khenchaf, A., Le Caillec, J.-M.: A methodology to evaluate important dimensions of information. ACM J. Data Inf. Qual. 6(2–3), 23 (2015). Article no. 11
Rogova, G.L.: Information quality in information fusion and decision making with applications to crisis management. In: Rogova, G., Scott, P. (eds.) Fusion Methodologies in Crisis Management, pp. 65–86. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-22527-2_4
Steinberg, A.N., Bowman, C.L., White, F.E.: Revisions to the JDL Data Fusion. Data Fusion Lex. by JDL (1991)
Wang, Y.R., Ziad, M., Lee, Y.W.: Data Quality. Kluwer Academic Publishers, Dordrecht (2002)
Ortega-Adarme, M., Moreno-Revelo, M., Peluffo-Ordoñez, D.H., Marín Castrillon, D., Castro-Ospina, A.E., Becerra, M.A.: Analysis of motor imaginary BCI within multi-environment scenarios using a mixture of classifiers. In: Solano, A., Ordoñez, H. (eds.) CCC 2017. CCIS, vol. 735, pp. 511–523. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66562-7_37
Mendes, P.N., Mühleisen, H., Bizer, C.: Sieve: linked data quality assessment and fusion, pp. 116–123 (2012)
Haug, A., Haug, A., Zachariassen, F., van Liempd, D.: The costs of poor data quality. J. Ind. Eng. Manag. 4(2), 168–193 (2011)
Cabitza, F., Batini, C.: Information quality in healthcare. Data and Information Quality. DSA, pp. 403–419. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-24106-7_13
Acknowledgments
This work was supported by the Doctoral thesis “Data fusion model oriented to information quality” at the “Universidad Nacional of Colombia”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Becerra, M.A., Alvarez-Uribe, K.C., Peluffo-Ordoñez, D.H. (2018). Low Data Fusion Framework Oriented to Information Quality for BCI Systems. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2018. Lecture Notes in Computer Science(), vol 10814. Springer, Cham. https://doi.org/10.1007/978-3-319-78759-6_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-78759-6_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78758-9
Online ISBN: 978-3-319-78759-6
eBook Packages: Computer ScienceComputer Science (R0)