Inter-individual deep image reconstruction via hierarchical neural code conversion

Semantic language decoding across participants and stimulus modalities

Brain decoders that reconstruct language from semantic representations have the potential to improve communication for people with impaired language production. However, training a semantic decoder for a participant currently requires many hours of brain responses to linguistic stimuli, and people with impaired language production often also have impaired language comprehension. In this study, we tested whether language can be decoded from a goal participant without using any linguistic training data from that participant. We trained semantic decoders on brain responses from separate reference participants and then used functional alignment to transfer the decoders to the goal participant. Cross-participant decoder predictions were semantically related to the stimulus words, even when functional alignment was performed using movies with no linguistic content. To assess how much semantic representations are shared between language and vision, we compared functional alignment accuracy using story and movie stimuli and found that performance was comparable in most cortical regions. Finally, we tested whether cross-participant decoders could be robust to lesions by excluding brain regions from the goal participant prior to functional alignment and found that cross-participant decoders do not depend on data from any single brain region. These results demonstrate that cross-participant decoding can reduce the amount of linguistic training data required from a goal participant and potentially enable language decoding from participants who struggle with both language production and language comprehension.

Mental image reconstruction from human brain activity: Neural decoding of mental imagery via deep neural network-based Bayesian estimation

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.