The electroencephalogram (EEG) signal is used as biometric modality because it is proven unique, universality and collectability. 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 amplitude and cross-correlation feature extraction methods were used to extract the EEG signals. The area under ROC curve (AUC) measurement of FRNN were promising against the D-kNN and FLR techniques. The FRNN model has achieved the best performance of AUC measure at 0.904 in oppose to the D-kNN and FLR models, where both recorded at 0.770 and 0.563 respectively. However, the classification accuracy shows significantly no different among the three classifiers. The results confirmed that the classification accuracy of D-kNN and FLR techniques are 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 to obtain a better data representation of biometric brainprint for more efficient authentication in imbalance data problem.
Key words: Fuzzy-rough nearest neighbour (FRNN), EEG, brainprint authentication, biometrics, brainwave analysis
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