Section: New Results

Decoding four letter words from brain activations

Participants : Bertrand Thirion, Alexandre Gramfort [Correspondant] .

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.

Figure 9. The bars of a word presented on a fixed visual brain activate specific domains of the visual field, and thus can be decoded through this marked. This makes it possible to identify a four letters word presented on a screen.

More details can be found in [22] .