Decoding syllables from human fMRI activity

Yohei Otaka, Rieko Osu, Mitsuo Kawato, Meigen Liu, Satoshi Murata, Yukiyasu Kamitani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Language plays essential roles in human cognition and social communication, and therefore technology of reading out speech using non-invasively measured brain activity will have both scientific and clinical merits. Here, we examined whether it is possible to decode each syllable from human fMRI activity. Four healthy subjects participated in the experiments. In a decoding session, the subjects repeatedly uttered a syllable presented on a screen at 3Hz for a 12-s block. Nine different syllables are presented in a single experimental run which was repeated 8 times. We also specified the voxels which showed articulation-related activities by utterance of all the syllables in Japanese phonology in a conventional task-rest sequence. Then, we used either all of these voxels or a part of these voxels that exist in anatomically specified ROIs (M1, cerebellum) during decoding sessions as data samples for training and testing a decoder (linear support vector machine) that classifies brain activity patterns for different syllables. To evaluate decoding performance, we performed cross-validation by testing the sample of one decoding session using a decoder trained with the samples of the remaining sessions. As a result, syllables were correctly decoded at above-chance levels. The results suggest the possibility of using non-invasively measured brain activity to read out the intended speech of disabled patients in speech motor control.

Original languageEnglish
Title of host publicationNeural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
Number of pages8
EditionPART 2
Publication statusPublished - 23-10-2008
Externally publishedYes
Event14th International Conference on Neural Information Processing, ICONIP 2007 - Kitakyushu, Japan
Duration: 13-11-200716-11-2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4985 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th International Conference on Neural Information Processing, ICONIP 2007

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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