Conclusions and Future Work
7.2 Future Work
groups is 25% for SpEn in delta and between the elderly and young groups 19.8% for the same method and in the same waveband. For gender, the impact is also obvious as the results show the differences between the two gender groups which reflect the changes in brain signals in relation to gender reported by many previous studies.
Again, SEn and CEn are still the best feature extraction methods when consider-ing the impact of age and gender on EEG-based person identification systems. This is because, besides maintaining the best accuracy rates compared to the other en-tropy methods, SEn and CEn were shown to be less affected by these factors than other methods. For example, SEn showed no difference among the age groups in the theta band since they all achieved 100% accuracy, and CEn had the smallest gaps in accuracy between gender groups as the maximum was only 2.8%.
7.2 Future Work
The report of entropy feature extraction methods for EEG-based automatic person identification are just initial results which need to be investigated more thoroughly.
Future work will focus on the following extensions of this study:
1. Continuing experiments on the five proposed entropy methods to find the best parameters and options for providing the best performance in person identifi-cation;
2. Extending the investigations to more entropy methods for feature extraction of EEG signals for automatic person identification. This has two benefits: a) it confirms the advantages of entropy for EEG-based feature extractions; b) it helps to find the best entropy feature extraction method;
3. Extending experiments on non-entropy methods for investigating impact of brain conditions and human characteristics on the performance of EEG-based person identification systems;
4. Continuing experiments on SVM, i.e., testing to find optimized parameters (classifier’s kernels and their relevant parameters) and extending the investi-gations to other classifiers such as Neural Networks, k-nearest Neighbor and so on. The aim of this would be to find the best classifier and parameters which
7.2 Future Work 133
could then be used for classifying entropy features in order to gain optimized performance of person identification;
5. Extending the tests to variable sample sizes to choose appropriate number of samples for automatic person identification.
6. Extending experiments to larger scale datasets both in size and types and to those recorded from future’s new specialised devices.
7. Employing more performance evaluation methods, for example the ANOVA test, precision, recall and F score to measure tests’ accuracy and to thoroughly investigate the impact of epilepsy, alcohol, age and genders on EEG-based per-son identification. In addition, the influences of emotion on EEG-based perper-son identification should be investigated in the same manner.
8. Extending the current research into automatic EEG-based person authentica-tion.
In brief, the aim of our future work would be to study an EEG-based automatic person identification/authentication system based on entropy features which:
1. provides the best performance in person identification;
2. is least affected by brain conditions or human characteristics (age and gender).
Bibliography
Ab´asolo, D., Hornero, R., Espino, P., Poza, J., S´anchez, C. I., and de la Rosa, R. (2005). Analysis of regularity in the eeg background activity of alzheimer’s disease patients with approximate entropy.
Clinical Neurophysiology, 116(8):1826–1834.
Abdullah, M. K., Subari, K. S., Loong, J. L. C., and Ahmad, N. N. (2010). Analysis of the eeg signal for a practical biometric system. World Academy of Science, Engineering and Technology, 68:1123–1127.
Acharya, U. R., Bhat, S., Adeli, H., Adeli, A., et al. (2014). Computer-aided diagnosis of alcoholism-related eeg signals. Epilepsy & Behavior, 41:257–263.
Acharya, U. R., Molinari, F., Sree, S. V., Chattopadhyay, S., Ng, K.-H., and Suri, J. S. (2012a).
Automated diagnosis of epileptic eeg using entropies. Biomedical Signal Processing and Control, 7(4):401–408.
Acharya, U. R., Sree, S. V., Chattopadhyay, S., and Suri, J. S. (2012b). Automated diagnosis of normal and alcoholic eeg signals. International journal of neural systems, 22(03):1250011.
Adeli, H., Ghosh-Dastidar, S., and Dadmehr, N. (2007). A wavelet-chaos methodology for analysis of eegs and eeg subbands to detect seizure and epilepsy. Biomedical Engineering, IEEE Transactions on, 54(2):205–211.
Akareddy, S. M. and Kulkarni, P. (2013). Eeg signal classification for epilepsy seizure detection using improved approximate entropy. International Journal of Public Health Science (IJPHS), 2(1):23–32.
Anand, S., Shanthaselvakumari, R., and Priya, C. (2013). Research review an automatic detection of epilepsy in human brain signal. International Journal of Advanced Computer Technology (IJACT), 2(5):43–50.
Anderson, C. W., Stolz, E., Shamsunder, S., et al. (1998). Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks. Biomedical Engineering, IEEE Transactions on, 45(3):277–286.
135
BIBLIOGRAPHY 136
Andrzejak, R. G., Widman, G., Lehnertz, K., Rieke, C., David, P., and Elger, C. (2001). The epileptic process as nonlinear deterministic dynamics in a stochastic environment: an evaluation on mesial temporal lobe epilepsy. Epilepsy research, 44(2):129–140.
Angulo, C., Parra, X., and Catala, A. (2003). K-svcr. a support vector machine for multi-class classification. Neurocomputing, 55(1):57–77.
Babiloni, F., Cichocki, A., and Gao, S. (2007). Brain-computer interfaces: towards practical imple-mentations and potential applications. Computational Intelligence and Neuroscience, 2007.
Balli, T. and Palaniappan, R. (2008). On the complexity and energy analyses in eeg between alcoholic and control subjects during delayed matching to sample paradigm. International Journal of Computational Intelligence and Applications, 7(03):301–315.
Bao, X., Wang, J., and Hu, J. (2009). Method of individual identification based on electroencephalo-gram analysis. In New Trends in Information and Service Science, 2009. NISS’09. International Conference on, pages 390–393. IEEE.
Barry, R. J., Clarke, A. R., McCarthy, R., Selikowitz, M., Johnstone, S. J., and Rushby, J. A.
(2004). Age and gender effects in eeg coherence: I. developmental trends in normal children.
Clinical neurophysiology, 115(10):2252–2258.
Begleiter, H. (1999). Eeg database.
Bezerianos, A., Tong, S., and Thakor, N. (2003). Time-dependent entropy estimation of eeg rhythm changes following brain ischemia. Annals of Biomedical Engineering, 31(2):221–232.
Bozdogan, H. (2000). Akaike’s information criterion and recent developments in information com-plexity. Journal of mathematical psychology, 44(1):62–91.
Brenner, R. P., Ulrich, R. F., and Reynolds, C. F. (1995). Eeg spectral findings in healthy, elderly men and womensex differences. Electroencephalography and clinical neurophysiology, 94(1):1–5.
Brunelli, R. and Falavigna, D. (1995). Person identification using multiple cues. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 17(10):955–966.
Bruzzo, A. A., Gesierich, B., Santi, M., Tassinari, C. A., Birbaumer, N., and Rubboli, G. (2008).
Permutation entropy to detect vigilance changes and preictal states from scalp eeg in epileptic patients. a preliminary study. Neurological Sciences, 29(1):3–9.
Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2):121–167.
Campisi, P., La Rocca, D., and Scarano, G. (2012). Eeg for automatic person recognition. Computer, 45(7):87–89.
BIBLIOGRAPHY 137
Carrier, J., Land, S., Buysse, D. J., Kupfer, D. J., and Monk, T. H. (2001). The effects of age and gender on sleep eeg power spectral density in the middle years of life (ages 20–60 years old).
Psychophysiology, 38(2):232–242.
Casdagli, M. C., Iasemidis, L. D., Savit, R. S., Gilmore, R. L., Roper, S. N., and Sackellares, J. C. (1997). Non-linearity in invasive eeg recordings from patients with temporal lobe epilepsy.
Electroencephalography and clinical Neurophysiology, 102(2):98–105.
Catarino, A., Churches, O., Baron-Cohen, S., Andrade, A., and Ring, H. (2011). Atypical eeg com-plexity in autism spectrum conditions: a multiscale entropy analysis. Clinical Neurophysiology, 122(12):2375–2383.
Chaovalitwongse, W. A., Prokopyev, O. A., and Pardalos, P. M. (2006). Electroencephalogram (eeg) time series classification: Applications in epilepsy. Annals of Operations Research, 148(1):227–250.
Clarke, A. R., Barry, R. J., McCarthy, R., and Selikowitz, M. (2001). Age and sex effects in the eeg:
development of the normal child. Clinical Neurophysiology, 112(5):806–814.
Das, K., Zhang, S., Giesbrecht, B., and Eckstein, M. P. (2009). Using rapid visually evoked eeg activity for person identification. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pages 2490–2493. IEEE.
De Araujo, D., Tedeschi, W., Santos, A., Elias, J., Neves, U., and Baffa, O. (2003). Shannon entropy applied to the analysis of event-related fmri time series. NeuroImage, 20(1):311–317.
Duffy, F. H., Albert, M. S., McAnulty, G., and Garvey, A. J. (1984). Age-related differences in brain electrical activity of healthy subjects. Annals of neurology, 16(4):430–438.
Duffy, F. H., McAnulty, G. B., and Albert, M. S. (1993). The pattern of age-related differences in electrophysiological activity of healthy males and females. Neurobiology of aging, 14(1):73–84.
Duffy, F. H., Mcanulty, G. B., and Albert, M. S. (1996). Effects of age upon interhemispheric eeg coherence in normal adults. Neurobiology of aging, 17(4):587–599.
Ehlers, C. L., Havstad, J., Prichard, D., and Theiler, J. (1998). Low doses of ethanol reduce evidence for nonlinear structure in brain activity. The Journal of neuroscience, 18(18):7474–7486.
Farwell, L. A. and Smith, S. S. (2001). Using brain mermer testing to detect knowledge despite efforts to conceal. Journal of Forensic Sciences, 46(1):135–143.
Faust, O., Acharya, R., Allen, A., and Lin, C. (2008). Analysis of eeg signals during epileptic and alcoholic states using ar modeling techniques. IRBM, 29(1):44–52.
BIBLIOGRAPHY 138
Faust, O., Yu, W., and Kadri, N. A. (2013). Computer-based identification of normal and alcoholic eeg signals using wavelet packets and energy measures. Journal of Mechanics in Medicine and Biology, 13(03):1350033.
Field, M., Wiers, R. W., Christiansen, P., Fillmore, M. T., and Verster, J. C. (2010). Acute alcohol effects on inhibitory control and implicit cognition: implications for loss of control over drinking.
Alcoholism: Clinical and Experimental Research, 34(8):1346–1352.
Fu, X. and Wang, L. (2003). Data dimensionality reduction with application to simplifying rbf network structure and improving classification performance. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 33(3):399–409.
Gabor, A., Leach, R., and Dowla, F. (1996). Automated seizure detection using a self-organizing neural network. Electroencephalography and clinical Neurophysiology, 99(3):257–266.
Gasser, T., Jennen-Steinmetz, C., Sroka, L., Verleger, R., and M¨ocks, J. (1988). Development of the eeg of school-age children and adolescents ii. topography. Electroencephalography and clinical neurophysiology, 69(2):100–109.
Giaquinto, S. and Nolfe, G. (1986). The eeg in the normal elderly: a contribution to the interpretation of aging and dementia. Electroencephalography and clinical neurophysiology, 63(6):540–546.
Glover Jr, J. R., Ktonas, P. Y., Raghavan, N., Urunuela, J. M., Velamuri, S. S., and Reilly, E. L.
(1986). A multichannel signal processor for the detection of epileptogenic sharp transients in the eeg. Biomedical Engineering, IEEE Transactions on, (12):1121–1128.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10–18.
Hall, M. A. (1998). Correlation-based feature subset selection for machine learning.
Hanlon, H. W., Thatcher, R. W., and Cline, M. J. (1999). Gender differences in the development of eeg coherence in normal children. Developmental Neuropsychology, 16(3):479–506.
Hesse, C. and James, C. (2005). Tracking and detection of epileptiform activity in multichannel ictal eeg using signal subspace correlation of seizure source scalp topographies. Medical and Biological Engineering and Computing, 43(6):764–770.
Hope, A. T. and Rosipal, R. (2000). Measuring depth of anesthesia using electroencephalogram entropy rates.
Hume, K., Van, F., and Watson, A. (1998). A field study of age and gender differences in habitual adult sleep. Journal of sleep research, 7(2):85–94.
BIBLIOGRAPHY 139
Hunter, M., Smith, R. L., Hyslop, W., Rosso, O. A., Gerlach, R., Rostas, J., Williams, D., and Henskens, F. (2005). The australian eeg database. Clinical EEG and neuroscience, 36(2):76–81.
Hytti, H., Takalo, R., and Ihalainen, H. (2006). Tutorial on multivariate autoregressive modelling.
Journal of clinical monitoring and computing, 20(2):101–108.
Iasemidis, L. D., Shiau, D.-S., Chaovalitwongse, W., Sackellares, J. C., Pardalos, P. M., Principe, J. C., Carney, P. R., Prasad, A., Veeramani, B., and Tsakalis, K. (2003). Adaptive epileptic seizure prediction system. Biomedical Engineering, IEEE Transactions on, 50(5):616–627.
James, C. J., Jones, R. D., Bones, P. J., and Carroll, G. J. (1999). Detection of epileptiform discharges in the eeg by a hybrid system comprising mimetic, self-organized artificial neural network, and fuzzy logic stages. Clinical Neurophysiology, 110(12):2049–2063.
Janvale, G., Kendre, S., and Mehrotra, S. (2014). Mental and behavioural disorders related to alcohol and their effects on eeg signals–an overview. Procedia-Social and Behavioral Sciences, 133:116–121.
Jian-feng, H. (2009). Multifeature biometric system based on eeg signals. In Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, pages 1341–1345. ACM.
Jiang, D. and Hu, J. (2009). Research of welch arithmetic and wavelet transforms for person identification of eeg. In Computer Science and Engineering, 2009. WCSE’09. Second International Workshop on, volume 2, pages 449–452. IEEE.
Kahkonen, S., Wilenius, J., Nikulin, V. V., Ollikainen, M., and Ilmoniemi, R. J. (2003). Alcohol reduces prefrontal cortical excitability in humans: a combined tms and eeg study. Neuropsy-chopharmacology, 28(4):747–754.
Kannathal, N., Acharya, U. R., Lim, C., and Sadasivan, P. (2005a). Characterization of eega comparative study. Computer methods and Programs in Biomedicine, 80(1):17–23.
Kannathal, N., Choo, M. L., Acharya, U. R., and Sadasivan, P. (2005b). Entropies for detection of epilepsy in eeg. Computer methods and programs in biomedicine, 80(3):187–194.
Katz, R. I. and Horowitz, G. R. (1982). Electroencephalogram in the septuagenarian: studies in a normal geriatric population. Journal of the American Geriatrics Society, 30(4):273–275.
Khan, Y. and Gotman, J. (2003). Wavelet based automatic seizure detection in intracerebral elec-troencephalogram. Clinical Neurophysiology, 114(5):898–908.
Kim, S., Kim, D.-J., and Jeong, J. (2007). The effect of alcohol on cortical complexity in healthy subjects measured by approximate entropy. In World Congress on Medical Physics and Biomedical Engineering 2006, pages 1091–1094. Springer.
BIBLIOGRAPHY 140
Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21(1):1–6.
Krishnaveni, V., Jayaraman, S., Kumar, P. M., Shivakumar, K., and Ramadoss, K. (2005). Com-parison of independent component analysis algorithms for removal of ocular artifacts from elec-troencephalogram. Measurement Science Review, 5(2):67–78.
Kuhlmann, L., Burkitt, A. N., Cook, M. J., Fuller, K., Grayden, D. B., Seiderer, L., and Mareels, I. M. (2009). Seizure detection using seizure probability estimation: comparison of features used to detect seizures. Annals of biomedical engineering, 37(10):2129–2145.
Kumar, Y. and Dewal, M. (2011). Complexity measures for normal and epileptic eeg signals using apen, sampen and sen. Int J Comput Commun Technol, 2:6–12.
Kumari, P. and Vaish, A. (2015). Brainwave based user identification system: A pilot study in robotics environment. Robotics and Autonomous Systems, 65:15–23.
Kurth, C., Gilliam, F., and Steinhoff, B. (2000). Eeg spike detection with a kohonen feature map.
Annals of Biomedical Engineering, 28(11):1362–1369.
Lehnertz, K. and Elger, C. (1995). Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. Electroencephalography and clinical Neurophysiology, 95(2):108–117.
Li, D.-C., Liu, C.-W., and Hu, S. C. (2011). A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets. Artificial Intelligence in Medicine, 52(1):45–52.
Li, X., Ouyang, G., and Richards, D. A. (2007). Predictability analysis of absence seizures with permutation entropy. Epilepsy research, 77(1):70–74.
Litscher, G. (2006). Electroencephalogram-entropy and acupuncture. Anesthesia & Analgesia, 102(6):1745–1751.
Liu, J., Wu, S., Wang, Z., and Chen, Z. (2013). Wavelet entropy and complexity analysis for drinkers’
eeg. Sensors & Transducers, 160(12):184.
Lotte, F., Congedo, M., L´ecuyer, A., and Lamarche, F. (2007). A review of classification algorithms for eeg-based brain–computer interfaces. Journal of neural engineering, 4.
Marcel, S. and Del Millan, J. R. (2007). Person authentication using brainwaves (eeg) and maximum a posteriori model adaptation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 29(4):743–752.
BIBLIOGRAPHY 141
Marosi, E., Harmony, T., S´anchez, L., Becker, J., Bernal, J., Reyes, A., de Le´on, A. E. D., Rodr´ıguez, M., and Fern´andez, T. (1992). Maturation of the coherence of eeg activity in normal and learning-disabled children. Electroencephalography and clinical Neurophysiology, 83(6):350–357.
Marple Jr, S. L. (1987). Digital spectral analysis with applications. Englewood Cliffs, NJ, Prentice-Hall, Inc., 1987, 512 p., 1.
Marshall, P. J., Bar-Haim, Y., and Fox, N. A. (2002). Development of the eeg from 5 months to 4 years of age. Clinical Neurophysiology, 113(8):1199–1208.
Meier, R., Dittrich, H., Schulze-Bonhage, A., and Aertsen, A. (2008). Detecting epileptic seizures in long-term human eeg: a new approach to automatic online and real-time detection and classi-fication of polymorphic seizure patterns. Journal of Clinical Neurophysiology, 25(3):119–131.
Mirzaei, A., Ayatollahi, A., Gifani, P., and Salehi, L. (2010). Eeg analysis based on wavelet-spectral entropy for epileptic seizures detection. In Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on, volume 2, pages 878–882. IEEE.
Mizuno, T., Takahashi, T., Cho, R. Y., Kikuchi, M., Murata, T., Takahashi, K., and Wada, Y.
(2010). Assessment of eeg dynamical complexity in alzheimers disease using multiscale entropy.
Clinical Neurophysiology, 121(9):1438–1446.
Mohammadi, G., Shoushtari, P., Molaee Ardekani, B., and Shamsollahi, M. B. (2006). Person identification by using ar model for eeg signals. In Proceeding of World Academy of Science, Engineering and Technology, volume 11, pages 281–285.
Morabito, F. C., Labate, D., La Foresta, F., Bramanti, A., Morabito, G., and Palamara, I. (2012).
Multivariate multi-scale permutation entropy for complexity analysis of alzheimers disease eeg.
Entropy, 14(7):1186–1202.
Nguyen, P., Le, T., Tran, D., Huang, X., and Sharma, D. (2010). Fuzzy support vector machines for age and gender classification. In Eleventh Annual Conference of the International Speech Communication Association.
Nguyen, P., Tran, D., Huang, X., and Ma, W. (2013a). Age and gender classification using eeg paralinguistic features. In Neural Engineering (NER), 2013 6th International IEEE/EMBS Con-ference on, pages 1295–1298. IEEE.
Nguyen, P., Tran, D., Huang, X., and Sharma, D. (2012). A proposed feature extraction method for eeg-based person identification. In The International Conference on Artificial Intelligence (ICAI 2012), USA.
BIBLIOGRAPHY 142
Nguyen, P., Tran, D., Vo, T., Huang, X., Ma, W., and Phung, D. (2013b). Eeg-based age and gender recognition using tensor decomposition and speech features. In Neural Information Processing, pages 632–639. Springer.
O’Boyle, D. J., Van, F., and Hume, K. I. (1995). Effects of alcohol, at two times of day, on eeg-derived indices of physiological arousal. Electroencephalography and clinical neurophysiology, 95(2):97–107.
Ocak, H. (2009). Automatic detection of epileptic seizures in eeg using discrete wavelet transform and approximate entropy. Expert Systems with Applications, 36(2):2027–2036.
Ochoa, J. B. (2002). Eeg signal classification for brain computer interface applications. Ecole Polytechnique Federale De Lausanne, 7:1–72.
O’Neill, N. S., Koles, Z. J., and Javidan, M. (2001). Identification of the temporal components of seizure onset in the scalp eeg. The Canadian Journal of Neurological Sciences, 28(03):245–253.
O’Regan, S., Faul, S., and Marnane, W. (2010). Automatic detection of eeg artefacts arising from head movements. In Engineering in Medicine and Biology Society (EMBC), 2010 Annual Inter-national Conference of the IEEE, pages 6353–6356. IEEE.
Oscar-Berman, M. and Marinkovi´c, K. (2007). Alcohol: effects on neurobehavioral functions and the brain. Neuropsychology review, 17(3):239–257.
Padma Shri, T. and Sriraam, N. (2012). Eeg based detection of alcoholics using spectral entropy with neural network classifiers.
Padma Shri, T., Sriraam, N., and Bhat, V. (2014). Characterization of eeg signals for identification of alcoholics using anova ranked approximate entropy and classifiers. In Circuits, Communication, Control and Computing (I4C), 2014 International Conference on, pages 109–112. IEEE.
Palaniappan, R. (2006). Electroencephalogram signals from imagined activities: A novel biometric identifier for a small population. In Intelligent Data Engineering and Automated Learning–IDEAL 2006, pages 604–611. Springer.
Palaniappan, R. and Mandic, D. P. (2007a). Biometrics from brain electrical activity: A machine learning approach. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 29(4):738–
742.
Palaniappan, R. and Mandic, D. P. (2007b). Eeg based biometric framework for automatic iden-tity verification. The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 49(2):243–250.
BIBLIOGRAPHY 143
Paranjape, R., Mahovsky, J., Benedicenti, L., and Koles, Z. (2001). The electroencephalogram as a biometric. In Electrical and Computer Engineering, 2001. Canadian Conference on, volume 2, pages 1363–1366. IEEE.
Parvinnia, E., Sabeti, M., Jahromi, M. Z., and Boostani, R. (2014). Classification of eeg signals using adaptive weighted distance nearest neighbor algorithm. Journal of King Saud University-Computer and Information Sciences, 26(1):1–6.
Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J., and Nichols, T. E. (2011). Statistical parametric mapping: the analysis of functional brain images: the analysis of functional brain images. Academic press.
Phung, D., Tran, D., Ma, W., Nguyen, P., and Pham, T. (2014a). Investigating the impacts of epilepsy on eeg-based person identification systems. In 2014 International Joint Conference on Neural Networks (IJCNN), pages 3644–3648. IEEE.
Phung, D., Tran, D., Ma, W., Nguyen, P., and Pham, T. (2014b). Using shannon entropy as eeg signal feature for fast person identification. In European Symposium on Artificial Neural Networks (ESANN), pages 413–418.
Phung, D., Tran, D., Ma, W., and Pham, T. (2015a). Conditional entropy approach to multichannel eeg-based person identification. In International Joint Conference, pages 157–165. Springer.
Phung, D., Tran, D., Ma, W., and Pham, T. (2015b). Investigating the impacts of brain conditions on eeg-based person identification. In International Joint Conference, pages 145–155. Springer.
Phung, D., Tran, D., Ma, W., and Pham, T. (2016). Investigating impacts of age and gender on eeg-based person identification systems. Submitted to European Symposium on Artificial Neural Networks (ESANN).
Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88(6):2297–2301.
Pollock, V., Schneider, L., and Lyness, S. (1990). Eeg amplitudes in healthy, late-middle-aged and elderly adults: normality of the distributions and correlations with age. Electroencephalography and clinical neurophysiology, 75(4):276–288.
Porjesz, B. and Begleiter, H. (2003). Alcoholism and human electrophysiology. Alcohol research and health, 27(2):153–160.
Poulos, M., Rangoussi, M., Alexandris, N., Evangelou, A., et al. (2002). Person identification from the eeg using nonlinear signal classification. Methods of information in Medicine, 41(1):64–75.
BIBLIOGRAPHY 144
Poulos, M., Rangoussi, M., Chrissikopoulos, V., and Evangelou, A. (1999a). Parametric person identification from the eeg using computational geometry. In Electronics, Circuits and Systems, 1999. Proceedings of ICECS’99. The 6th IEEE International Conference on, volume 2, pages 1005–1008. IEEE.
Poulos, M., Rangoussi, M., Chrissikopoulos, V., and Evangelou, A. (1999b). Person identification based on parametric processing of the eeg. In Electronics, Circuits and Systems, 1999. Proceedings of ICECS’99. The 6th IEEE International Conference on, volume 1, pages 283–286. IEEE.
Poulos, M. Rangoussi, N. A. A. E. M. (2001). On the use of eeg features towards person identification via neural networks. Informatics for Health and Social Care, 26(1):35–48.
Proakis, J. and Manolakis, D. (1996). Digital signal processing, prentice hall. Upper Saddle River, NJ.
Quian Quiroga, R., Rosso, O. A., Ba¸sar, E., and Sch¨urmann, M. (2001). Wavelet entropy in event-related potentials: a new method shows ordering of eeg oscillations. Biological cybernetics, 84(4):291–299.
Rangaswamy, M., Porjesz, B., Chorlian, D. B., Choi, K., Jones, K. A., Wang, K., Rohrbaugh, J., O’Connor, S., Kuperman, S., Reich, T., et al. (2003). Theta power in the eeg of alcoholics.
Alcoholism: Clinical and Experimental Research, 27(4):607–615.
Rescher, B. and Rappelsberger, P. (1999). Gender dependent eeg-changes during a mental rotation task. International Journal of Psychophysiology, 33(3):209–222.
Rosso, O. A., Blanco, S., Yordanova, J., Kolev, V., Figliola, A., Sch¨urmann, M., and Ba¸sar, E.
(2001). Wavelet entropy: a new tool for analysis of short duration brain electrical signals. Journal of neuroscience methods, 105(1):65–75.
Saab, M. and Gotman, J. (2005). A system to detect the onset of epileptic seizures in scalp eeg.
Clinical Neurophysiology, 116(2):427–442.
Sabesan, S., Chakravarthy, N., Tsakalis, K., Pardalos, P., and Iasemidis, L. (2009). Measuring resetting of brain dynamics at epileptic seizures: application of global optimization and spatial synchronization techniques. Journal of combinatorial optimization, 17(1):74–97.
Sabeti, M., Katebi, S., and Boostani, R. (2009). Entropy and complexity measures for eeg sig-nal classification of schizophrenic and control participants. Artificial Intelligence in Medicine, 47(3):263–274.
Sand, T., Br˚athen, G., Michler, R., Brodtkorb, E., Helde, G., and Bovim, G. (2002). Clinical utility of eeg in alcohol-related seizures. Acta neurologica scandinavica, 105(1):18–24.