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| Presentation | 2022-03-04 14:45 EEG classification with Aligners and Adversarial Domain Adaptation for Invariant Feature Extraction and Calibration Across Subjects Tatsuhiro Shiraishi (NAIST), Reinmar Kober, Kazuaki Kawanabe (ATR)  NC2021-75 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Due to non-stationarity and inter-subject difference, conventional machine learning methods are yet to achieve a breakthrough in EEG classification performance.Calibrating the difference between data from multiple subjects (domain adaptation) is critical for constructing better classification models.We propose an EEG classification model with aligners (compensation for inter-subject difference) and adversarial domain adaptation (ADA).Our objective is two-fold: to extract subject invariant classification features and to calibrate the model for a new subject using resting-state EEG.This imposes little burden on the user and is beneficial for real-world applications.In our preliminary analysis, we found that the model with aligners and ADA can extract latent features that are discriminative across classes but indiscriminative across subjects, and it could outperform the baseline model without aligners and ADA. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Brain Computer Interface (BCI) / machine learning / domain adaptation / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 390, NC2021-75, pp. 149-154, March 2022. | |
| Paper # | NC2021-75 | |
| Date of Issue | 2022-02-23 (NC) | |
| ISSN | 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 | NC2021-75 |
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| Conference Information | |
| Committee | MBE NC |
| Conference Date | 2022-03-02 - 2022-03-04 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2022-03-MBE-NC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | EEG classification with Aligners and Adversarial Domain Adaptation for Invariant Feature Extraction and Calibration Across Subjects |
| Sub Title (in English) | |
| Keyword(1) | Brain Computer Interface (BCI) |
| Keyword(2) | machine learning |
| Keyword(3) | domain adaptation |
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| 1st Author's Name | Tatsuhiro Shiraishi |
| 1st Author's Affiliation | Nara Institute of Science and Technology (NAIST) |
| 2nd Author's Name | Reinmar Kober |
| 2nd Author's Affiliation | Advanced Telecommunications Research Institute International (ATR) |
| 3rd Author's Name | Kazuaki Kawanabe |
| 3rd Author's Affiliation | Advanced Telecommunications Research Institute International (ATR) |
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| Speaker | Author-1 |
| Date Time | 2022-03-04 14:45:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2021-75 |
| Volume (vol) | vol.121 |
| Number (no) | no.390 |
| Page | pp.149-154 |
| #Pages | 6 |
| Date of Issue | 2022-02-23 (NC) |
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