The Brain as Target Image Detector: The Role of Image Category and Presentation Time

  • Anne-Marie Brouwer
  • Jan B. F. van Erp
  • Bart Kappé
  • Anne E. Urai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6780)


The brain can be very proficient in classifying images that are hard for computer algorithms to deal with. Previous studies show that EEG can contribute to sorting shortly presented images in targets and non-targets. We examine how EEG and classification performance are affected by image presentation time and the kind of target: humans (a familiar category) or kangaroos (unfamiliar). Humans are much easier detected as indicated by behavioral data, EEG and classifier performance. Presentation of humans is reflected in the EEG even if observers were attending to kangaroos. In general, 50ms presentation time decreased markers of detection compared to 100ms.


Event Related Potential Target Type Equal Error Rate Brain Computer Interface Human Target 
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  1. 1.
    Thorpe, S., Fize, D., Marlot, C.: Speed of processing in the human visual system. Nature 381, 520–522 (1996)CrossRefGoogle Scholar
  2. 2.
    Goffaux, V., Jacques, C., Mouraux, A., Oliva, A., Schyns, P.G., Rossion, B.: Diagnostic colours contribute to the early stages of scene categorization: Behavioural and neurophysiological evidence. Vis. Cogn. 12, 878–892 (2005)CrossRefGoogle Scholar
  3. 3.
    Farwell, L.A., Donchin, E.: Talking off the top of your head: A mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology 70, 510–523 (1988)CrossRefGoogle Scholar
  4. 4.
    Sajda, P., Gerson, A., Parra, L.: High-throughput image search via single-trial event classification in a rapid serial visual presentation task. In: Proc. First International IEEE EMBS Conference on Neural Engineering, pp. 7–10 (2003)Google Scholar
  5. 5.
    Gerson, A.D., Parra, L.C., Sajda, P.: Cortically-coupled Computer Vision for Rapid Image Search. IEEE Trans. on Neural Systems & Rehabilitation Engineering 14, 174–179 (2006)CrossRefGoogle Scholar
  6. 6.
    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. In: Dornhege, G., Mueller, K.-R. (eds.) Brain-Computer Interface. MIT Press, Cambridge (2007)Google Scholar
  7. 7.
    Parra, L.C., Christoforou, C., Gerson, A.D., Dyrholm, M., Luo, A., Wagner, M., Philiastides, M.G., Sajda, P.: Spatio-temporal linear decoding of brain state: Application to performance augmentation in high-throughput tasks. IEEE Signal Processing Magazine 25, 95–115 (2008)CrossRefGoogle Scholar
  8. 8.
    Huang, Y., Erdogmus, D., Mathan, S., Pavel, M.: Comparison of Linear and Nonlinear Approaches on Single Trial ERP Detection in Rapid Serial Visual Presentation Tasks. In: International Joint Conference on Neural Networks, pp. 1136–1142 (2006)Google Scholar
  9. 9.
    Huang, Y., Erdogmus, D., Mathan, S., Pavel, M.: A Fusion Approach for Image Triage using Single Trial ERP Detection. In: 3rd International IEEE/EMBS Conference on Neural Engineering, pp. 473–476 (2007)Google Scholar
  10. 10.
    Huang, Y., Erdogmus, D., Mathan, S., Pavel, M.: Large-scale image database triage via EEG evoked responses. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 429–432 (2008)Google Scholar
  11. 11.
    Bentin, S., McCarthy, G., Perez, E., Puce, A., Allison, T.: Electrophysiological studies of face perception in humans. J. Cogn. Neurosci. 8, 551–565 (1996)CrossRefGoogle Scholar
  12. 12.
    Rossion, B., Jacques, C.: Does physical interstimulus variance account for early electrophysiological face sensitive responses in the human brain? Ten lessons on the N170. NeuroImage 39, 1959–1979 (2008)CrossRefGoogle Scholar
  13. 13.
    Busey, T.A., Vanderkolk, J.R.: Behavioral and electrophysiological evidence for configural processing in fingerprint experts. Vis. Res. 45, 431–448 (2005)CrossRefGoogle Scholar
  14. 14.
    Griffin, G., Holub, A., Perona, P.: Caltech-256 Object Category Dataset. California Institute of Technology (2007),
  15. 15.
    Jasper, H.: Report of the committee on methods of clinical examination in electroencephalography. Electroencephalography and Clinical Neurophysiology 10, 370–375 (1958)CrossRefGoogle Scholar
  16. 16.
    Ravden, D., Polich, J.: On P300 measurement stability: habituation, intra-trial block variation, and ultradian rhythms. Biol. Psych. 51, 59–76 (1999)CrossRefGoogle Scholar
  17. 17.
    Bandt, C., Weymar, M., Samaga, D., Hamm, A.O.: A simple classification tool for single-trial analysis of ERP components. Psychophysiol. 46, 747–757 (2009)CrossRefGoogle Scholar
  18. 18.
    Rousselet, G.A., Macé, M.J., Fabre-Thorpe, M.: Animal and human faces in natural scenes: How specific to human faces is the N170 ERP component? JOV 4, 13–21 (2004)CrossRefGoogle Scholar
  19. 19.
    Rossion, B., Joyce, C.J., Cottrell, G.W., Tarr, M.J.: Early lateralization and orientation tuning for face, word and object processing in the visual cortex. Neuroimage 20, 1609–1624 (2003)CrossRefGoogle Scholar
  20. 20.
    Kanwisher, N., McDermott, J., Chun, M.M.: The fusiform face area: A module in human extrastriate cortex specialized for face perception. J. Neurosci. 17(11), 4302–4311 (1997)Google Scholar
  21. 21.
    Luck, S.J.: An Introduction to the Event-Related Potential Technique. MIT Press, Cambridge (2005)Google Scholar
  22. 22.
    Shenoy, P., Tan, D.S.: Human-Aided Computing: Utilizing Implicit Human Processing to Classify Images. In: CHI 2008 Proceedings Cognition, Perception, and Memory (2008)Google Scholar
  23. 23.
    Yazdani, A., Vesin, J.-M., Izzo, D., Ampatzis, C., Ebrahimi, T.: Implicit retrieval of salient images using brain computer interface. In: Proceedings of International Conference on Image Processing, ICIP (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Anne-Marie Brouwer
    • 1
  • Jan B. F. van Erp
    • 1
  • Bart Kappé
    • 1
  • Anne E. Urai
    • 1
    • 2
  1. 1.TNO Human FactorsSoesterbergThe Netherlands
  2. 2.University College UtrechtUtrechtThe Netherlands

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