Classifying mental activities from EEG-P300 signals using adaptive neural networks

International Journal of Innovative Computing, Information and Control

Volumn 8, Issue 9, 2012, Pages 6429-6443

  Turnip, Arjon ^a   Hong, Keum Shik ^a  

^a Pusan National University   (South Korea)

Author keywords

Adaptive high order neural network; Brain computer interface; Classification; EEG P300 potentials; Feature extraction

Indexed keywords

ADAPTIVE NEURAL NETWORKS; AR MODELS; AUTO-REGRESSIVE; BACK PROPAGATION NEURAL NETWORKS; CLASSIFICATION SCHEME; EEG SIGNALS; HIGH-ORDER NEURAL NETWORK; MENTAL ACTIVITY; NONSTATIONARY DATA; PERFORMANCE IMPROVEMENTS;

BRAIN COMPUTER INTERFACE; CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION;

NEURAL NETWORKS;

EID: 84866022706     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (33)

1. J. J. Vidal, Toward direct brain-computer communication, Annual Review of Biophysics and Bioengineering, vol.2, pp.157-180, 1973.
2. T. Yamaguchi, K. Nagala, P. Q. Truong, M. Fujio, K. Inoue and G. Pfurtscheller, Pattern recognition of EEG signal during motor imagery by using SOM, International Journal of Innovative Computing, Information and Control, vol.4, no.10, pp.2617-2630, 2008.
3. M. Hamalainen, R. Hari, R. J. Ilmoniemi, J. Knuutila and O. V. Lounasmaa, Magnetoencephalography-Theory, instrumentation, and applications to noninvasive studies of the working human brain, Reviews of Modern Physics, vol.65, no.2, pp.413-497, 1993.
4. B. E. Hillner, B. A. Siegel, D. Liu, A. F. Shields, I. F. Gareen, L. Hanna, S. H. Stine and R. E. Coleman, Impact of positron emission tomography/computed tomography and positron emission tomography (pet) alone on expected management of patients with cancer: Initial results from the national oncologic pet registry, Journal of Clinical Oncology, vol.26, no.13, pp.2155-2161, 2008.
5. U. Hoffmann, J.-M. Vesin and T. Ebrahimi, An efficient P300-based brain-computer interface for disabled subjects, Journal of Neuroscience Methods, vol.167, no.1, pp.115-125, 2008.
6. C. R. Hema, M. P. Paulraj, R. Nagarajan, S. Taacob and A. H. Adom, Brain machine interface: A comparison between fuzzy and neural classiérs, International Journal of Innovative Computing, Information and Control, vol.5, no.7, pp.1819-1827, 2009.
7. A. Turnip, K.-S. Hong and M.-Y. Jeong, Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis, BioMedical Engineering OnLine, vol.10, no.83, 2011.
8. R. M. Chapman and H. R. Bragdon, Evoked responses to numerical and nonnumerical visual stimuli while problem solving, Nature, vol.203, no.12, pp.1155-1157, 1964.
9. S. Sutton, M. Braren, E. R. John and J. Zubin, Evoked potential correlates of stimulus uncertainty, Science, vol.150, no.700, pp.1187-1188, 1965.
10. M. Onofrj, D. Melchionda, A. Thomas and T. Fulgente, Reappearance of event-related P3 potential in locked-in syndrome, Cognitive Brain Research, vol.4, no.2, pp.95-97, 1996.
11. L. A. Farwell and E. Donchin, Talking off the top of the head: Toward a mental prosthesis utilizing event-related brain potentials, Electroenceph. Clin. Neurophysiol., vol.70, no.6, pp.510-523, 1988.
12. J. Kronegg, G. Chanel, S. Voloshynovskiy and T. Pun, EEG-based synchronized brain-computer interfaces: A model for optimizing the number of mental tasks, IEEE Trans. Neural Syst. Rehabil. Eng., vol.15, no.1, pp.50-58, 2007.
13. B. Obermaier, C. Neuper, C. Guger and G. Pfurtscheller, Information transfer rate in a five-classes brain-computer interface, IEEE Trans. Neural Syst. Rehabil. Eng., vol.9, no.3, pp.282-288, 2001.
14. P. J. Brockwell and R. A. Davis, Time Series: Theory and Methods, 2nd Edition, Springer Science, Business Media, LLC, New York, 2006.
15. X.-S. Hu, K.-S. Hong, S. S. Ge and M.-Y. Jeong, Kalman estimatorand general linear model-based on-line brain activation mapping by near-infrared spectroscopy, BioMedical Engineering OnLine, vol.9, no.82, 2010.
16. S. Erla, L. Faes, E. Tranquillini, D. Orrico and G. Nollo, Multivariate autoregressive model with instantaneous effects to improve brain connectivity estimation, International Journal of Bioelectromagnetism, vol.11, no.2, pp.74-79, 2009.
17. A. Adam, M. I. Shapiai, Z. Ibrahim, M. Khalid and L. W. Jau, Development of a hybrid artificial neural network-Naive bayes classiér for binary classification problem of imbalanced datasets, ICIC Express Letters, vol.5, no.9(A), pp.3171-3176, 2011.
18. A. Jaffar, B. Ahmed, N. Naveed, A. Hussain, F. Jabeen and A. M. Mirza, Multi domain features based classification of mammogram images using SVM and MLP, ICIC Express Letters, vol.4, no.3(B), pp.937-942, 2010.
19. G. Onishi, S. Ishimitsu, K. Sakamoto, T. Yoshimi, Y. Fujimoto and K. Kawasaki, Automatic evaluation of button sound impressions using a neural network, ICIC Express Letters, vol.4, no.3(A), pp.683-689, 2010.
20. S. S. Ge, T. H. Lee and T. Zhang, Adaptive neural network control for a class of discrete-time non-linear systems, International Journal Control, vol.76, no.4, pp.334-354, 2003.
21. E. B. Kosmatopoulos, M. M. Polycarpou, M. A. Christodoulou and P. A. Ioannou, High-order neural network structures for identification of dynamical systems, IEEE Trans. Neural Netw., vol.6, no.2, pp.422-431, 1995.
22. X.-S. Hu, K.-S. Hong and S. S. Ge, Recognition of stimulus-evoked neuronal optical response by identifying chaos levels of near-infared spectroscopy time series, Neuroscience Letters, vol.504, no.2, pp.115-120, 2011.
23. M. Wang, S. S. Ge and K.-S. Hong, Approximation-based adaptive tracking control of pure-feedback nonlinear systems with multiple unknown time-varying delays, IEEE Transactions on Neural Networks, vol.21, no.11, pp.1804-1816, 2010.
24. F. Girosi and T. Poggio, Networks and the Best Approximation Property, Artificial Intelligence Laboratory Memorandum, no.1164, MIT, Cambridge, 1989.
25. L. Giles and T. Maxwell, Learning, invariance and generalization in high order neural networks, Applied Optics, vol.26, no.23, pp.4972-4978, 1987.
26. Y. Nishihara, J. Irie, T. Yamaguchi, T. Yamazaki and K. Inoue, The statistical estimation method of muscle activity based on surface EEG, ICIC Express Letters, Part B: Applications, vol.2, no.3, pp.603-608, 2011.
27. B. You, H. Wang and L. Zhou, The study of BP neural network classification based on extension, ICIC Express Letters, vol.5, no.9(B), pp.3411-3416, 2011.
28. J. Wang, N. Chen and K. Zhang, Music emotional classification research based on BP neural network, ICIC Express Letters, vol.4, no.6(A), pp.2075-2079, 2010.
29. J. R. Wolpaw, N. Birbaumer, W. J. Heetderks, D. J. McFarland, P. H. Peckham, G. Schalk, E. Donchin, L. A. Quatrano, C. J. Robinson and T. M. Vaughan, Brain-computer interface technology: A review of the first international meeting, IEEE Trans. Rehabil. Eng., vol.8, no.2, pp.164-173, 2000.
30. D. J. McFarland, W. A. Sarnacki and J. R. Wolpaw, Brain-computer interface (BCI) operation: Optimizing information transfer rates, Biological Psychology, vol.63, no.3, pp.237-251, 2003.
31. X.-S. Hu, K.-S. Hong and S. S. Ge, Recognition of stimulus-evoked neuronal optical response by identifying chaos levels of near-infrared spectroscopy time series, Neuroscience Letters, vol.504, no.2, pp.115-120, 2011.
32. X.-S. Hu, K.-S. Hong and S. S. Ge, fNIRS-based online deception decoding, Journal of Neural Engineering, vol.9, no.2, pp.1-8, 2012.
33. M. Aqil, K.-S. Hong, M.-Y. Jeong and S. S. Ge, Cortical brain imaging by adaptive filtering of NIRS signals, Neuroscience Letters, vol.514, no.1, pp.35-41, 2012.