Non Linear Features Analysis between Imaginary and Non-imaginary Tasks for Human EEG-based Biometric Identification

• Published in: IOP conference series. Materials Science and Engineering Vol. 557; no. 1; pp. 12033 - 12037
• Main Authors: Ying Ong, Zhi, Saidatul, A, Vijean, V, Ibrahim, Z
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.06.2019
• Subjects: Biometric identification; Biometrics; Brain; Classification; Electroencephalography; Empirical analysis; Feature extraction; Notch filters; Power lines; Signal processing
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/557/1/012033

Electroencephalogram (EEG) is a signal contains information of brain activities. Nowadays, many types of research regarding EEG have been done such as neuromarketing. The human brain is very complicated but it contains a lot of information. EEG signal is a non-stationary signal, it changes over time and it also depends on human's emotion, thinking and activities. Due to the uniqueness of the EEG signal, the EEG signal has the potential to be used in the human authentication system. In this paper, an imaginary task and a non-imaginary task were studied to find out which type of task is possible to be used in authentication system. In preliminary study, five subjects were volunteered and performed the motor imagery and motor execution tasks. EEGOTM sports device (ANT Neuro, Enschede, Netherlands) with 32 channels was used to record the EEG signal and the sampling frequency is set to 512 Hz. The EEG signal was analysed by using EEG signal processing namely pre-processing, feature extraction and classification. Power line interference was removed by using a notch filter. Daubechies 8 wavelet family with 5th level decomposition has been applied to remove baseline wander noise. The performance of non-linear features such as Empirical Mode Decomposition (EMD), Hurst Exponent and Entropy were examined. Random forest gives good classification accuracy for imaginary task and non-imaginary task which are 83.53% and 87.06% respectively, thus, it shows non-linear features is possible to be employed in biometric identification.