Paper Abstract and Keywords |
Presentation |
2013-09-24 13:55
Speech Classification Using Brain Activity Signals Atsushi Nishimoto, Natsue Yoshimura, Koji Jimura (Tokyo Inst. of Tech./NCNP), Hiroyuki Kambara, Duk Shin (Tokyo Inst. of Tech.), Takashi Hanakawa (NCNP), Yasuharu Koike (Tokyo Inst. of Tech.) MBE2013-43 NC2013-29 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Recently, Brain-Computer Interfaces (BCI) are used for handicapped individuals such as patients that have difficulty in communicating. In this study, we explored the possibilities of non-invasive BCIs for speech identification. Based on electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), EEG cortical current signals were estimated using Variational Bayesian Multimodal EncephaloGraphy (VBMEG) method. The results of experiments with Japanese vowels /a/ and /i/ showed that classification accuracies might be increased when using anatomical-based functional region of interests (ROIs). |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Brain-computer Interface / EEG / fMRI / vowel / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 113, no. 223, NC2013-29, pp. 41-46, Sept. 2013. |
Paper # |
NC2013-29 |
Date of Issue |
2013-09-17 (MBE, NC) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
Download PDF |
MBE2013-43 NC2013-29 |
|