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Neural Network Classification of Word Evoked Neuromagnetic Brain Activity

  • Ramin Assadollahi
  • Friedemann Pulvermüller
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2036)

Abstract

The brain-physiological signatures of words are modulated by their psycholinguistic and physical properties. The fine-grained differences in complex spatio-temporal patterns of a single word induced brain response may, nevertheless, be detected using unsupervised neuronal networks. Objective of this study was to motivate and explore an architecture of a Kohonen net and its performance, even when physical stimulus properties are kept constant over the classes.

We investigated 16 words from four lexico-semantic classes. The items from the four classes were matched for word length and frequency. A Kohonen net was trained on the data recorded from a single subject. After learning, the network performed above chance on new testing data: In the recognition of the neuromagnetic signal from individual words its recognition rate was 28% above chance (Chi-square = 16.3, p<0.0001) and its accuracy was 44% above chance (Chi-square = 40.8, p<0.0001). The classification of brain responses into lexico-semantic classes was also unexpectedly high (recognition rate 16% above chance, Chi-square = 27.2, p<0.0001, accuracy 20% above chance, Chi-square = 42.0, p<0.0001).

Our results suggest that research on single trial recognition of brain responses is feasible and a rich field to explore.

Keywords

Recognition Rate Word Length Brain Response Function Word Word Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Pulvermüller F. Behav. Brain Sci., 22, 253–336. (1999)CrossRefGoogle Scholar
  2. 2.
    Pulvermüller F, Lutzenberger W, Birbaumer N. Electroenceph. Clin. Neurophysiol.,94, 357–370 (1995)CrossRefGoogle Scholar
  3. 3.
    Osterhout L, Bersick M, McKinnon R. Biol. Psychol., 46, 143–168 (1997)CrossRefGoogle Scholar
  4. 4.
    Rugg, M.D. Mem. Cogn. 18, 367–379 (1990)Google Scholar
  5. 5.
    Suppes P, Bing H, Lu Z-L, PNAS, 95, 15861–15866, (1998)CrossRefGoogle Scholar
  6. 6.
    Suppes P, Lu Z-L, Bing H, PNAS, 94, 14965–14969, (1997)CrossRefGoogle Scholar
  7. 7.
    Kohonen T, Biological Cybernetics, 43, 59–69 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Kohonen T, Makissra K, Saramaki T, In: Proc. of the Seventh Int. Conf. on Pattern Recognition, Montreal, Canada, 182–185 (1984)Google Scholar
  9. 9.
    Kohonen T, Hari R, TINS, 22/3, 135–139 (1999)Google Scholar
  10. 10.
    Damasio H, Human brain anatomy in computerized images, Oxford University Press, New York, Oxford. (1995)Google Scholar
  11. 11.
    Lutzenberger W, Pulvermüller F, Birbaumer N, Neuroscience Letters, 176, 115–118 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ramin Assadollahi
    • 1
  • Friedemann Pulvermüller
    • 2
  1. 1.Department of PsychologyUniversity of KonstanzGermany
  2. 2.Medical Research CouncilCognition and Brain Sciences UnitCambridgeEngland

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