A framework for rapid visual image search using single-trial brain evoked responses

Neurocomputing

Volumn 74, Issue 12-13, 2011, Pages 2041-2051

  Huang, Yonghong ^a   Erdogmus, Deniz ^a,b   Pavel, Misha ^a   Mathan, Santosh ^c   Hild, Kenneth E ^a  

^a Oregon Health and Science University Department of Biomedical Engineering   (United States)

^b Northeastern University   (United States)

^c HONEYWELL TECHNOLOGY CENTER   (United States)

Author keywords

Brain computer interface (BCI); Electroencephalography (EEG); Image retrieval; Incremental learning; Single trial event related potential (ERP)

Indexed keywords

BRAIN RESPONSE; COMPUTATIONAL COSTS; DETECTION PERFORMANCE; EVENT-RELATED POTENTIALS; EVOKED RESPONSE; HUMAN DYNAMICS; IMAGE ANALYSTS; INCREMENTAL LEARNING; LINEAR SVM; MEMORY STORAGE; NONLINEAR SUPPORT VECTOR MACHINES; RAPID SERIAL VISUAL PRESENTATIONS; TARGET DETECTION; TARGET IMAGES; VISUAL IMAGE;

ELECTROENCEPHALOGRAPHY; ELECTROPHYSIOLOGY; IMAGE ANALYSIS; IMAGE RETRIEVAL; INTERFACES (COMPUTER); SEARCH ENGINES; SUPPORT VECTOR MACHINES;

BRAIN COMPUTER INTERFACE;

ARTICLE; BATCH LEARNING; BRAIN COMPUTER INTERFACE; CLASSIFIER; COMPUTER MEMORY; CONTROLLED STUDY; ELECTROENCEPHALOGRAM; EVENT RELATED POTENTIAL; HUMAN; IMAGE RETRIEVAL; INCREMENTAL LEARNING; LEARNING; LINEAR LOGISTIC CLASSIFIER; PRIORITY JOURNAL; RETINA IMAGE; SUPPORT VECTOR MACHINE; VISUAL INFORMATION;

EID: 79956096489     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.12.025     Document Type: Article

References (34)

1. Campbell K.B., Courchesne E., Picton T.W., Squires K.C. Evoked potential correlations of human information processing. Biological Psychology 1979, 8(1):45-68.
2. Bigdely-Shamlo N., Vankov A., Ramirez R.R., Makeig S. Brain activity-based image classification from rapid serial visual presentation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2008, 16(5):432-441.
3. Scholkopf B., Smola A.J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond 2001, MIT Press, New Jersey, NY.
4. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998, 2:121-167.
5. Chapin J.K., Moxon K.A., Markowitz R.S., Nicolelis M.A. Real-time control of a robot arm using simultaneous recorded neurons in the motor cortex. Nature Neurosciences 1999, 2:664-670.
6. Chun M.M., Potter M.C. A two-stage model for multiple target detection in rapid serial visual presentation. Journal of Experimental Psychology: Human Perception and Performance 1995, 21(1):109-127.
7. Cortes C., Vapnik V. Support-vector networks. Machine Learning 1995, 20:273-297.
8. Cristianini N., Shawe-Taylor J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods 2000, Cambridge University Press, Cambridge, UK.
9. DeLong E.R., DeLong D.M., Clarke-Pearson D.L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988, 44:837-845.
10. Gerson A.D., Parra L.C., Sajda P. Cortical origins of response time variability during rapid discrimination of visual objects. Neuroimage 2005, 28(2):342-353.
11. Hosmer D.W., Lemeshow S. Applied Logistic Regression 2000, Wiley-Interscience.
12. Y. Huang, D. Erdogmus, S. Mathan, M. Pavel, A fusion approach for image triage using single trial ERP detection, in: Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering, Kohala Coast, HI, 2007, pp. 473-476.
13. Y. Huang, D. Erdogmus, S. Mathan, M. Pavel, Detecting EEG evoked responses for target image search with mixed effect models, in: Proceedings of the 30th International IEEE EMBS Conference, Vancouver, BC, Canada, 2008, pp. 4988-4991.
14. Johnson J.S., Olshausen B.A. Timecourse of neural signatures of object recognition. Journal of Vision 2003, 3:499-512.
15. Keysers C., -K Xiao D., Foldiak P., Perrett D.I. The speed of sight. Journal of Cognitive Neuroscience 2001, 13(1):90-101.
16. Krauledat M., Tangermann M., Blankertz B., Muller K.R. Towards zero training for brain-computer interfacing. PLoS ONE 2008, 3(8):e2967.
17. Laird N.M., Ware J.H. Random-effect models for longitudinal data. Biometrics 1982, 38:963-974.
18. Lebedev M.A., Nicolelis M.A.L. Brain-machine interfaces: past, present and future. Trends in Neurosciences 2006, 29(9):536-546.
19. Luck S.J. An Introduction to the Event-Related Potential Technique 2005, The MIT Press, Cambridge, MA.
20. Makeig S., Westerfield M., Jung T.P., Enghoff S., Townsend J. Dynamic brain sources of visual evoked responses. Science 2002, 295:690-694.
21. Oken B.S. Evoked Potentials in Clinical Medicine 1997, LippincottRaven, Philadelphia, PA, Chapter 15.
22. E. Osuna, R. Freund, F. Girosi, Support vector machines: training and applications, Technical Report AIM-1602, 1997.
23. Parra L.C., Spence C.D., Gerson A.D., Sajda P. Recipes for the linear analysis of eeg. Neuroimage 2005, 28(2):326-341.
24. Pepe M.S. The Statistical Evaluation of Medical Tests for Classification and Prediction 2003, Oxford University Press, New York, NY.
25. Picton T.W. The p300 wave of the human event-related potential. Journal of Clinical Neurophysiology 1992, 9(4):456-479.
26. Potter M.C., Levy E.I. Recognition memory for a rapid sequence of pictures. Journal of Experimental Psychology 1969, 81(1):10-15.
27. Sajda P., Gerson A.D., Philiastides M.G., Parra L.C. Single-trial analysis of EEG during rapid visual discrimination: enabling cortically coupled computer vision. Towards Brain-Computer Interfacing 2007, MIT Press.
28. Sajda P., Parra L.C., Christoforou C., Hanna B., Bahlmann C., Wang J., Pohlmeyer E., Dmochowski J., Chang S.-F. In a blink of an eye and a switch of a transistor: cortically-coupled computer vision. Proceedings of the IEEE 2010, 98(3):462-478.
29. Schoroger E., Widmann A. Speeded responses to audiovisual signal changes result from bimodal integration. Psychophysiology 1998, 35:755-759.
30. N.A. Syed, H. Liu, K.K. Sung, Incremental learning with support vector machines, in: Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 1999.
31. Thorpe S., Fize D., Marlot C. Speed of processing in the human visual system. Nature 1996, 381:520-522.
32. VanRullen R., Thorpe S.J. The time course of visual processing: from early perception to decision-making. Journal of Cognitive Neuroscience 2001, 13(4):454-461.
33. Vapnik V.N. Statistical Learning Theory 1998, John Wiley and Sons Inc., New York, NY.
34. Wolpaw J.R. Brain-machine interfaces for communication and control: a mini-review. Clinical Neurophysiology 2004, 57:607-613.