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Abstract:
Can a self-organising neuronal network classify single
neuromagnetic recordings following presentation of individual
words? There is evidence that spatio-temporal characteristics of
the (averaged) brain response reflect word length and frequency
(Osterhout et al. 1997) and aspects of word semantics
(Pulvermüller 1999). Words from four categories were chosen:
function words, action verbs, nouns primarily eliciting visual
associations, and nouns with both strong visual and action
associations. Word length (one/two syllables) and frequency
(high/low) were also varied in an orthogonal design. The sixteen
words (4 categories x 2 lengths x 2 frequencies) were presented
visually to a single subject whose brain response was recorded by
an 148 channel MEG. A neuronal net (Kohonen 1982) was trained on
the field strength of 8 regions of interest. 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 word
categories 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). We conclude that unsupervised
Kohonen maps can classify single word-evoked brain
responses.
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