| Budget Amount *help |
¥196,690,000 (Direct Cost: ¥151,300,000、Indirect Cost: ¥45,390,000)
Fiscal Year 2024: ¥38,610,000 (Direct Cost: ¥29,700,000、Indirect Cost: ¥8,910,000)
Fiscal Year 2023: ¥38,610,000 (Direct Cost: ¥29,700,000、Indirect Cost: ¥8,910,000)
Fiscal Year 2022: ¥38,610,000 (Direct Cost: ¥29,700,000、Indirect Cost: ¥8,910,000)
Fiscal Year 2021: ¥38,610,000 (Direct Cost: ¥29,700,000、Indirect Cost: ¥8,910,000)
Fiscal Year 2020: ¥42,250,000 (Direct Cost: ¥32,500,000、Indirect Cost: ¥9,750,000)
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| Outline of Final Research Achievements |
This research has established a technology for visualizing diverse mental images by integrating brain activity measurement and the latent representation of deep neural networks. Specifically, it has succeeded in reconstructing subjective perceptions as images, such as objects of attention or illusory perceptions that differ from physical stimuli. Furthermore, it has developed an inter-individual image reconstruction technique that eliminates the need for large amounts of training data for each subject. Exceeding initial expectations, the research also succeeded in extending this technology to modalities other than vision. Additionally, it has addressed the issue of "spurious reconstruction" seen with the use of generative AI by conducting theoretical analysis and building a framework to ensure reliability. These achievements open new avenues for objectively capturing human subjective experiences.
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