Progress Towards Decoding Visual Imagery via fNIRS
This work addresses the challenge of decoding visual imagery for brain-computer interface applications, but it is incremental as it builds on existing fMRI-based methods.
The paper tackled the problem of reconstructing images from fNIRS brain activity by training a model on downsampled fMRI data, achieving 71% retrieval accuracy with 1-cm resolution compared to 93% on full-resolution fMRI.
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.