Explainable Model Differencing for MEG Decoding via Predict‐Probability Differences and Boundary‐Optimized Rules

Deep neural networks are driving the iterative advancement of magnetoencephalography (MEG) decoding models. While explainable artificial intelligence, particularly traditional post-hoc feature attribution approaches, has made significant progress in interpreting the prediction mechanisms of individual models, a critical gap remains in understanding the differences in decision logic between various models, known as model differencing. By facilitating model selection, optimization updates, and pra