Abstract
Modern biometric identification methods combine interdisciplinary approaches to enhance person identification and classification accuracy. One popular technique for this purpose is Brain-Computer Interface (BCI). The signal so obtained from BCI will be further processed by the Autoregressive (AR) Model for feature extraction. Many researches in the area find that for more accurate results, the signal must be cleaned before extracting any useful feature information. This study proposes Independent Component Analysis (ICA), k-NN classifier, and AR as the combined techniques for electroencephalogram (EEG) biometrics to achieve the highest personal identification and classification accuracy. However, there is a classification gap between using the combined ICA with the AR model and AR model alone. Therefore, this study takes one step further by modifying the feature extraction of AR and comparing the outcome with the proposed approaches in lieu of prior researches. The experiment based on four relevant locations shows that the combined ICA and AR can achieve higher accuracy than the modified AR. More combinations of channels and subjects are required in future research to explore the significance of channel effects and to enhance the identification accuracy.
Highlights
Biometrics is a person authentication and identification technique using several real organs
25. end for Classification Accuracy of 20 Subjects with 128 Data Point Length Performed on the Independent Component Analysis (ICA) with the AR Method for 5-fold and 10-fold Cross Validation with k-Nearest Neighbor (k-NN) having k=1, 3 and 5
K-NN was chosen as the main classifier along with the 5-fold and 10-fold cross validation for evaluating the classification. k was set to Classification Accuracy of 20 Subjects with 256 Data Point Length Performed on the ICA with the AR Method and Only the AR Method for 10-fold Cross Validation with k-NN having k=1
Summary
Biometrics is a person authentication and identification technique using several real organs. Biometrics function can be divided into two steps: identification and authentication. The first step performs verification and validation of individuals in a database or a group of persons. The functional area in the human brain is divided into four areas, namely frontal, parietal, temporal and occipital lobes. Many researches have focused on person authentication based on these EEG rhythms (Campisi et al, 2011; Rocca et al, 2012). Many researches on ERP using EEG biometrics obtain satisfactor high accuracy (Kumari & Vaish, 2016; Ruiz-Blonded et al, 2016). We set out to explore these combined techniques in this study to attain higher person identification accuracy. The experimental results are described, and some final thoughts are given in the conclusion and future work section
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