Explainable Model Differencing for MEG Decoding via Predict-Probability Differences and Boundary-Optimized Rules
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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 interpreti... ...