Accurate linear modeling of EEG-based cortical activity during a passive motor task with input: a sub-space identification approach
Sanna Bakels, Mark van de Ruit, Matin Jafarian
公開日: 2025/10/2
Abstract
This paper studies linear mathematical modeling of brain's cortical dynamics using electroencephalography (EEG) data in an experiment with continuous exogenous input. The EEG data were recorded while participants were seated with their wrist strapped to a haptic manipulator. The manipulator imposed a continuous multisine angular perturbation to the wrist as the exogenous input to the brain. We show that subspace identification, in particular the PO-MOESP algorithm, leads to a linear time-invariant state-space model that accurately represents the measurements, in a latent space, assuming that the EEG data are the models' output. The model is verified and validated using data from seven participants. Moreover, we construct linear maps to relate the latent space dynamics to the neural source space. We show that findings by our model align with those identified in previous studies.