Interpretable Artificial Intelligence in Brain Function Research Tasks

Existing deep neural networks for decoding brain activity prioritize performance over interpretability, failing to link the decision rule to cortical sources and the dynamic properties of their electrical activity. Conversely, traditional neuroimaging identifies neural substrates behind behavior-specific brain states but relies on oversimplified models unable to capture the complexity of brain activity variations. Our approach integrates interpretable neural networks with source-level cortical dynamics, bridging these gaps to reveal physiologically meaningful patterns that differentiate complex brain states, enabling us to build compact yet powerful decoders and mine potentially novel neurophysiological knowledge.