Advances in Signal Processing and Artificial Intelligence: Proceedings of the 7th International Conference on Advances in Signal Processing and Artificial Intelligence 8-10 April 2025 Innsbruck, Austria Edited by Sergey Y. Yurish

Accurately modeling effective connectivity (EC) is critical for understanding how the brain processes and integrates sensory information. Yet, it remains a formidable challenge due to complex neural dynamics and noisy measurements such as those obtained from the electroencephalogram (EEG). Model-driven EC infers local (within a brain region) and global (between brain regions) EC parameters by fitting a generative model of neural activity onto experimental data. This approach offers a promising route for various applications, including investigating neurodevel- opmental disorders. However, current approaches fail to scale to whole-brain analyses and are highly noise-sensitive. In this work, we employ three deep-learning architectures—a transformer, a long short-term memory (LSTM) net- work, and a convolutional neural network and bidirectional LSTM (CNN-BiLSTM) network—for inverse modeling and compare their performance with simulation-based inference in estimating the Jansen-Rit neural mass model (JR- NMM) parameters from simulated EEG data under various noise conditions. We demonstrate a reliable estimation of key local parameters, such as synaptic gains and time constants. However, other parameters like local JR-NMM connectivity cannot be evaluated reliably from evoked-related potentials (ERP). We also conduct a sensitivity analysis to characterize the influence of JR-NMM parameters on ERP and evaluate their learnability. Our results show the feasibility of deep-learning approaches to estimate the subset of learnable JR-NMM parameters.