Low Data Fusion Framework Oriented to Information Quality for BCI Systems
- 1.2k Downloads
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.
KeywordsBrain-computer interface Data fusion Evaluation system Information quality
This work was supported by the Doctoral thesis “Data fusion model oriented to information quality” at the “Universidad Nacional of Colombia”.
- 5.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)Google Scholar
- 9.Rogova, G.L., Bosse, E.: Information quality in information fusion. In: 2010 13th International Conference on Information Fusion, pp. 1–8 (2010)Google Scholar
- 10.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)Google Scholar
- 11.van Laere, J.: Challenges for IF performance evaluation in practice. In: 2009 12th International Conference on Information Fusion, FUSION 2009, pp. 866–873 (2009)Google Scholar
- 15.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)Google Scholar
- 16.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. 11Google Scholar
- 18.Steinberg, A.N., Bowman, C.L., White, F.E.: Revisions to the JDL Data Fusion. Data Fusion Lex. by JDL (1991)Google Scholar
- 20.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_37CrossRefGoogle Scholar
- 21.Mendes, P.N., Mühleisen, H., Bizer, C.: Sieve: linked data quality assessment and fusion, pp. 116–123 (2012)Google Scholar
- 22.Haug, A., Haug, A., Zachariassen, F., van Liempd, D.: The costs of poor data quality. J. Ind. Eng. Manag. 4(2), 168–193 (2011)Google Scholar