Impact of Ageing on EEG Based Biometric Systems

With the development of sensor technology, Electroencephalography (EEG) has been a popular area of interest in recent years. Also, a great degree of changes with age have been found in face, voice, fingerprint or other physiological based biometric identifier systems. The distinct characteristic of neuro-signals have focused the attention of research community towards building a user identification system which is resistant to vulnerable attacks. However, the permanence issue of the brain signals has been studied sporadically. In this paper, we investigate the robustness of EEG signals to address the longitudinal stability issue and its effectiveness in user identification systems. Discrete Wavelet Transform (DWT) signal decomposition technique has been applied to extract Alpha-band waves. Further, two statistical features, namely, Root Mean Square (RMS) and Integrated EEG (IEEG) have been calculated for the band waves. Person identification has been performed using three well-known classification techniques, namely, Random Forest (RF), Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN). EEG data from 10 users has been recorded in 6 different sessions within a period of 6 months. Finally, a decision fusion scheme, majority voting has been applied to boost the system performance. An average accuracy of 80% has been recorded using decision fusion. The results highlight a significant amount of variations across sessions, which shows various factors could effect the state of the mind with temporal With the development of sensor technology, Electroencephalography (EEG) has been a popular area of interest in recent years. Also, a great degree of changes with age have been found in face, voice, fingerprint or other physiological based biometric identifier systems. The distinct characteristic of neuro-signals have focused the attention of research community towards building a user identification system which is resistant to vulnerable attacks. However, the permanence issue of the brain signals has been studied sporadically. In this paper, we investigate the robustness of EEG signals to address the longitudinal stability issue and its effectiveness in user identification systems. Discrete Wavelet Transform (DWT) signal decomposition technique has been applied to extract Alpha-band waves. Further, two statistical features, namely, Root Mean Square (RMS) and Integrated EEG (IEEG) have been calculated for the band waves. Person identification has been performed using three well-known classification techniques, namely, Random Forest (RF), Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN). EEG data from 10 users has been recorded in 6 different sessions within a period of 6 months. Finally, a decision fusion scheme, majority voting has been applied to boost the system performance. An average accuracy of 80% has been recorded using decision fusion. The results highlight a significant amount of variations across sessions, which shows various factors could effect the state of the mind with temporal distance. Keywords: EEG, Biometrics, Ageing, Random Forest, Majority Voting.