Fuzzy-Rough Classification for Brainprint Authentication

The electroencephalogram (EEG) signal is used as biometric modality, because it is proven to be unique, universal and collectable. This work aims to assess the performance of fuzzy-based techniques for brainprint authentication modelling. We benchmark the performance of Fuzzy-Rough Nearest Neighbour (FRNN) technique to the Discernibility Nearest Neighbour (D-kNN) and the Fuzzy Lattice Reasoning (FLR) techniques using the selected samples of brainwaves’ data from the original UCI EEG dataset. All the three classifiers are available in the fuzzy-rough version of WEKA implementation tool. Selected 9 EEG channels located at the midline and lateral regions were used in the experimentation. The coherence, mean of amplitudes and cross-correlation feature extraction methods were used to extract the EEG signals. The area under ROC curve (AUC) measurement of FRNN was promising against the D-kNN and FLR techniques. The FRNN model has achieved the best performance of AUC measure at 0.904 in opposition to the D-kNN and FLR models, where both recorded 0.770 and 0.563, respectively. However, the classification accuracy shows significantly no difference among the three classifiers. The results confirmed that the classification accuracy of D-kNN and FLR techniques is not reliable, because they are highly contributed by the true negative cases. Hence, we conclude that the FRNN model is less biased to imbalance data problem as compared to the D-kNN and FLR models. Future work of this research should focus on optimizing the EEG channel and feature selection in order to obtain a better data representation of biometric brainprint for more efficient authentication in imbalance data problem.