LEARNING SUBJECT STABLE CORTICAL DYNAMICS FROM EEG WITH EVENT SYNCHRONIZED INVARIANT MODES

EEG based motor decoding is usually built around a separation that is convenient computationally but weak scientifically. Source imaging is used to estimate where cortical activity occurs, while classification is applied afterward to decide what movement was performed. For fine upper limb actions, this separation is problematic because the most anatomically complete reconstruction is not necessarily the representation that transfers best across people. We introduce Event Synchronized Invariant Cortical Modes, ESICM, which learns cortical patterns that are simultaneously tied to the forward model, aligned with movement timing, discriminative across actions, and stable across subjects. Instead of reconstructing a dense source map and then compressing it for prediction, ESICM learns a small set of cortical modes directly from training data through a leadfield constrained generalized eigen problem with subject variability and cortical smoothness penal- ties. Multiband EEG is projected into these modes and decoded by a lightweight temporal network, producing a compact cortical representation without a parallel sensor branch or post hoc sensor source fusion. All supervised operations are con- fined to the training subjects under nested leave one subject out evaluation, with classical inverse methods retained as anatomical references. The resulting framework treats source structure, cross subject stability, and efficient decoding as a single representation problem, providing a direct path from scalp EEG to compact cortical dynamics suitable for embedded brain computer interfaces.