Extended Data Fig. 8. Decoding performance at lower spatial resolutions.

While fMRI provides high spatial resolution, current MRI scanners are too large and expensive for most practical decoder applications. Portable alternatives like functional near-infrared spectroscopy (fNIRS) measure the same hemodynamic activity as fMRI, albeit at a lower spatial resolution. To simulate how the decoder would perform at lower spatial resolutions, fMRI data were spatially smoothed using Gaussian kernels with standard deviations of 1, 2, 3, 4, and 5 voxels, corresponding to 6.1, 12.2, 18.4, 24.5, and 30.6 mm full width at half maximum (FWHM). The encoding model, noise model, and word rate model were estimated on spatially smoothed training data, and the decoder was evaluated on spatially smoothed responses to the perceived speech test story. (a) fMRI images for each subject were spatially smoothed using progressively larger Gaussian kernels. Blue voxels have above average activity and red voxels have below average activity. (b) Story similarity decreased as the data were spatially smoothed, but remained high at moderate levels of smoothing. (c) The fraction of significantly decoded time-points decreased as the data were spatially smoothed, but remained high at moderate levels of smoothing. (d) Encoding model prediction performance increased as the data were spatially smoothed, demonstrating that decoding performance and encoding model performance are not perfectly coupled. While spatial smoothing reduces information, making it harder to decode the stimulus, it also reduces noise, making it easier to predict the responses. For all results, black lines indicate the mean across subjects and error bars indicate the standard error of the mean ($n=3$). Dashed gray lines indicate the estimated spatial resolution of current portable systems⁴³. These results show that around 50% of the stimulus time-points could still be decoded at the estimated spatial resolution of current portable systems, and provide a benchmark for how much portable systems need to improve to reach different levels of decoding performance.