EEG‐based biometric authentication modelling using incremental fuzzy‐rough nearest neighbour technique

This paper proposes an Incremental Fuzzy‐Rough Nearest Neighbour (IncFRNN) technique for biometric authentication modelling using feature extracted visual evoked. Only small training set is needed for model initialisation. The embedded heuristic update method adjusts the knowledge granules incrementally to maintain all representative electroencephalogram (EEG) signal patterns and eliminate those rarely used. It reshapes the personalized knowledge granules through insertion and deletion of a test object, based on similarity measures. A predefined window size can be used to reduce the overall processing time. This proposed algorithm was verified with test data from 37 healthy subjects. Signal pre‐processing steps on segmentation, filtering and artefact rejection were carried out to improve the data quality before model building. The experimental paradigm was designed in three different conditions to evaluate the authentication performance of the IncFRNN technique against the benchmarked incremental K‐Nearest Neighbour (KNN) technique. The performance was measured in terms of accuracy, area under the Receiver Operating Characteristic (ROC) curve (AUC) and Cohen's Kappa coefficient. The proposed IncFRNN technique is proven to be statistically better than the KNN technique in the controlled window size environment. Future work will focus on the use of dynamic data features to improve the robustness of the proposed model.