Neuro signals: A future biomertic approach towards user identification

摘要: Electroencephalography (EEG) have been receiving a lot of attention due to its recent use in the field biometrics. Signals traced from different parts brain has become an upsurge area interest for researchers. Evidences provided by research communities where uniqueness neuro-signals can possibly be used building robust biometric identification system. In this paper, we investigate robustness EEG signals two scenario data collection, namely, Eyes Open (EO) and Closed (EC) person For this, publicly available dataset 109 users used. The modeled using classifier, Support Vector Machine (SVM) Random Forest (RF). Next, feature selection approach applied reduce number features results computed find optimal dimension. From experiments, rates 97.64% 96.02% SVM, 98.16% 97.30% recorded RF classifiers.