Low Data Fusion Framework Oriented to Information Quality for BCI Systems

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.