Progress Towards Decoding Visual Imagery via fNIRS
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913400833966080 |
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| author | Adamic, Michel Avelino, Wellington Brandenberger, Anna Chiang, Bryan Davis, Hunter Fay, Stephen Gregory, Andrew Gupta, Aayush Hotter, Raphael Jiang, Grace Leng, Fiona Polcyn, Stephen Ribeiro, Thomas Scotti, Paul Wang, Michelle Xiong, Marley Xu, Jonathan |
| author_facet | Adamic, Michel Avelino, Wellington Brandenberger, Anna Chiang, Bryan Davis, Hunter Fay, Stephen Gregory, Andrew Gupta, Aayush Hotter, Raphael Jiang, Grace Leng, Fiona Polcyn, Stephen Ribeiro, Thomas Scotti, Paul Wang, Michelle Xiong, Marley Xu, Jonathan |
| contents | We demonstrate the possibility of reconstructing images from fNIRS brain activity and start building a prototype to match the required specs. By training an image reconstruction model on downsampled fMRI data, we discovered that cm-scale spatial resolution is sufficient for image generation. We obtained 71% retrieval accuracy with 1-cm resolution, compared to 93% on the full-resolution fMRI, and 20% with 2-cm resolution. With simulations and high-density tomography, we found that time-domain fNIRS can achieve 1-cm resolution, compared to 2-cm resolution for continuous-wave fNIRS. Lastly, we share designs for a prototype time-domain fNIRS device, consisting of a laser driver, a single photon detector, and a time-to-digital converter system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07662 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Progress Towards Decoding Visual Imagery via fNIRS Adamic, Michel Avelino, Wellington Brandenberger, Anna Chiang, Bryan Davis, Hunter Fay, Stephen Gregory, Andrew Gupta, Aayush Hotter, Raphael Jiang, Grace Leng, Fiona Polcyn, Stephen Ribeiro, Thomas Scotti, Paul Wang, Michelle Xiong, Marley Xu, Jonathan Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Neurons and Cognition We demonstrate the possibility of reconstructing images from fNIRS brain activity and start building a prototype to match the required specs. By training an image reconstruction model on downsampled fMRI data, we discovered that cm-scale spatial resolution is sufficient for image generation. We obtained 71% retrieval accuracy with 1-cm resolution, compared to 93% on the full-resolution fMRI, and 20% with 2-cm resolution. With simulations and high-density tomography, we found that time-domain fNIRS can achieve 1-cm resolution, compared to 2-cm resolution for continuous-wave fNIRS. Lastly, we share designs for a prototype time-domain fNIRS device, consisting of a laser driver, a single photon detector, and a time-to-digital converter system. |
| title | Progress Towards Decoding Visual Imagery via fNIRS |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2406.07662 |