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アイテム
Mental image reconstruction from human brain activity: Neural decoding of mental imagery via deep neural network-based Bayesian estimation.
https://repo.qst.go.jp/records/2000915
https://repo.qst.go.jp/records/2000915344e276c-0694-42ec-bcef-b1773df12118
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2025-05-01 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Mental image reconstruction from human brain activity: Neural decoding of mental imagery via deep neural network-based Bayesian estimation. | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
| 資源タイプ | journal article | |||||||||||
| 著者 |
Naoko Koide-Majima
× Naoko Koide-Majima
× Shinji Nishimoto
× Kei Majima
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Visual images observed by humans can be reconstructed from their brain activity. However, the visualization (externalization) of mental imagery is challenging. Only a few studies have reported successful visualization of mental imagery, and their visualizable images have been limited to specific domains such as human faces or alphabetical letters. Therefore, visualizing mental imagery for arbitrary natural images stands as a significant milestone. In this study, we achieved this by enhancing a previous method. Specifically, we demonstrated that the visual image reconstruction method proposed in the seminal study by Shen et?al. (2019) heavily relied on low-level visual information decoded from the brain and could not efficiently utilize the semantic information that would be recruited during mental imagery. To address this limitation, we extended the previous method to a Bayesian estimation framework and introduced the assistance of semantic information into it. Our proposed framework successfully reconstructed both seen images (i.e., those observed by the human eye) and imagined images from brain activity. Quantitative evaluation showed that our framework could identify seen and imagined images highly accurately compared to the chance accuracy (seen: 90.7%, imagery: 75.6%, chance accuracy: 50.0%). In contrast, the previous method could only identify seen images (seen: 64.3%, imagery: 50.4%). These results suggest that our framework would provide a unique tool for directly investigating the subjective contents of the brain such as illusions, hallucinations, and dreams. | |||||||||||
| 書誌情報 |
Neural networks : the official journal of the International Neural Network Society 巻 170, p. 349-363, 発行日 2023-11 |
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| 出版者 | ||||||||||||
| 出版者 | Elsevier | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||
| 収録物識別子 | 1879-2782 | |||||||||||
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| 識別子タイプ | PMID | |||||||||||
| 関連識別子 | 38016230 | |||||||||||
| DOI | ||||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | 10.1016/j.neunet.2023.11.024 | |||||||||||
