Conditional Entropy Approach to Multichannel EEG-Based Person Identification

Person identification using electroencephalogram (EEG) as biometric has been widely used since it is capable of achieving high identification rate. Since single-channel EEG signal does not provide sufficient information for person identification, multi-channel EEG signals are used to record brain activities distributed over the entire scalp. However extracting brain features from multi-channel EEG signals is still a challenge. In this paper, we propose to use Conditional Entropy (CEN) as a feature extraction method for multi-channel EEG-based person identification. The use of entropy-based method is based on the fact that EEG signal is complex, non-linear, and random in nature. CEN is capable of quantifying how much uncertainty an EEG channel has if the outcome of another EEG channel is known. The mechanism of CEN in correlating pairs of channels would be a solution for feature extraction from multi-channel EEG signals. Our experimental results on EEG signals from 80 persons have shown that CEN provides higher identification rate, yet less number of features than the baseline Autoregressive modelling method.

[1]  Roberto Brunelli,et al.  Person identification using multiple cues , 1995, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  C.W. Anderson,et al.  Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks , 1998, IEEE Transactions on Biomedical Engineering.

[3]  Shogo Nishida,et al.  Autoregressive spectral analysis and model order selection criteria for EEG signals , 2000, 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119).

[4]  H. Bozdogan,et al.  Akaike's Information Criterion and Recent Developments in Information Complexity. , 2000, Journal of mathematical psychology.

[5]  L. Benedicenti,et al.  The electroencephalogram as a biometric , 2001, Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555).

[6]  M Poulos,et al.  Person Identification from the EEG using Nonlinear Signal Classification , 2002, Methods of Information in Medicine.

[7]  Lionel Torres,et al.  Person Identification Technique Using Human Iris Recognition , 2002 .

[8]  U. Rajendra Acharya,et al.  Entropies for detection of epilepsy in EEG , 2005, Comput. Methods Programs Biomed..

[9]  O. Rosso,et al.  The Australian EEG Database , 2005, Clinical EEG and neuroscience.

[10]  G. Litscher.  Electroencephalogram-Entropy and Acupuncture , 2006, Anesthesia and analgesia.

[11]  Heimo Ihalainen,et al.  Tutorial on Multivariate Autoregressive Modelling , 2006, Journal of clinical monitoring and computing.

[12]  W. Art Chaovalitwongse,et al.  Electroencephalogram (EEG) time series classification: Applications in epilepsy , 2006, Ann. Oper. Res..

[13]  Mohammad Bagher Shamsollahi,et al.  Person Identification by Using AR Model for EEG Signals , 2007 .

[14]  U. Rajendra Acharya,et al.  Erratum to "Entropies for detection of epilepsy in EEG" [Comput. Methods Prog. Biomed. 80 (2005) 187-194] , 2006, Comput. Methods Programs Biomed..

[15]  José del R. Millán,et al.  Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[17]  Saeid Sanei,et al.  EEG signal processing , 2000, Clinical Neurophysiology.

[18]  Lian-Wen Jin,et al.  Activity recognition from acceleration data using AR model representation and SVM , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[19]  Ling Huang,et al.  Feature Extraction of EEG Signals Using Power Spectral Entropy , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[20]  Nurul Nadia Ahmad,et al.  Analysis of the EEG Signal for a Practical Biometric System , 2010 .

[21]  Howida AbdelFattah Shedeed.  A new method for person identification in a biometric security system based on brain EEG signal processing , 2011, 2011 World Congress on Information and Communication Technologies.

[22]  Jin Xu,et al.  Feature extraction and classification of Event-related EEG based on Kolmogorov entropy , 2011, 2011 4th International Congress on Image and Signal Processing.

[23]  U. Rajendra Acharya,et al.  Author's Personal Copy Biomedical Signal Processing and Control Automated Diagnosis of Epileptic Eeg Using Entropies , 2022 .

[24]  Xufei Zheng,et al.  Feature Extraction of Motor Imagery in BCI with Approximate Entropy , 2012 .

[25]  Dharmendra Sharma,et al.  A Proposed Feature Extraction Method for EEG-based Person Identification , 2012 .

[26]  Tien Pham,et al.  Using Shannon Entropy as EEG Signal Feature for Fast Person Identification , 2014, ESANN.

[27]  Tien Pham,et al.  Investigating the impacts of epilepsy on EEG-based person identification systems , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).