Decoding Visual Percepts Induced by Word Reading with fMRI

Alexandre Gramfort (1, 2, 3) , Christophe Pallier (4) , Gaël Varoquaux (1, 3) , Bertrand Thirion (1, 3)

1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
2 Athinoula A. Martinos Center for Biomedical Imaging
3 LNAO - Laboratoire de Neuroimagerie Assistée par Ordinateur
4 UNICOG-U992 - Neuroimagerie cognitive - Psychologie cognitive expérimentale

Word reading involves multiple cognitive processes. To infer which word is being visualized, the brain first processes the visual percept, deciphers the letters, bigrams, and activates different words based on context or prior expectation like word frequency. In this contribution, we use supervised machine learning techniques to decode the first step of this processing stream using functional Magnetic Resonance Images (fMRI). We build a decoder that predicts the visual percept formed by four letter words, allowing us to identify words that were not present in the training data. To do so, we cast the learning problem as multiple classification problems after describing words with multiple binary attributes. This work goes beyond the identification or reconstruction of single letters or simple geometrical shapes and addresses a challenging estimation problem, that is the prediction of multiple variables from a single observation, hence facing the problem of learning multiple predictors from correlated inputs.

Mots clés: fMRI; classification; machine learning; brain reading; decoding

Domaines: Imagerie médicale

Alexandre Gramfort, Christophe Pallier, Gaël Varoquaux, Bertrand Thirion. Decoding Visual Percepts Induced by Word Reading with fMRI. Pattern Recognition in NeuroImaging (PRNI), 2012 International Workshop on, Jul 2012, Londres, United Kingdom. pp.13-16, ⟨10.1109/PRNI.2012.20⟩. ⟨hal-00730768⟩