  Despite advancements in artificial intelligence, object recognition models
still lag behind in emulating visual information processing in human brains.
Recent studies have highlighted the potential of using neural data to mimic
brain processing; however, these often rely on invasive neural recordings from
non-human subjects, leaving a critical gap in understanding human visual
perception. Addressing this gap, we present, for the first time,
'Re(presentational)Al(ignment)net', a vision model aligned with human brain
activity based on non-invasive EEG, demonstrating a significantly higher
similarity to human brain representations. Our innovative image-to-brain
multi-layer encoding framework advances human neural alignment by optimizing
multiple model layers and enabling the model to efficiently learn and mimic
human brain's visual representational patterns across object categories and
different modalities. Our findings suggest that ReAlnet represents a
breakthrough in bridging the gap between artificial and human vision, and
paving the way for more brain-like artificial intelligence systems.
