  Objective
  Kalman filtering has previously been applied to track neural model states and
parameters, particularly at the scale relevant to EEG. However, this approach
lacks a reliable method to determine the initial filter conditions and assumes
that the distribution of states remains Gaussian. This study presents an
alternative, data-driven method to track the states and parameters of neural
mass models (NMMs) from EEG recordings using deep learning techniques,
specifically an LSTM neural network.
  Approach
  An LSTM filter was trained on simulated EEG data generated by a neural mass
model using a wide range of parameters. With an appropriately customised loss
function, the LSTM filter can learn the behaviour of NMMs. As a result, it can
output the state vector and parameters of NMMs given observation data as the
input.
  Main Results
  Test results using simulated data yielded correlations with R squared of
around 0.99 and verified that the method is robust to noise and can be more
accurate than a nonlinear Kalman filter when the initial conditions of the
Kalman filter are not accurate. As an example of real-world application, the
LSTM filter was also applied to real EEG data that included epileptic seizures,
and revealed changes in connectivity strength parameters at the beginnings of
seizures.
  Significance
  Tracking the state vector and parameters of mathematical brain models is of
great importance in the area of brain modelling, monitoring, imaging and
control. This approach has no need to specify the initial state vector and
parameters, which is very difficult to do in practice because many of the
variables being estimated cannot be measured directly in physiological
experiments. This method may be applied using any neural mass model and,
therefore, provides a general, novel, efficient approach to estimate brain
model variables that are often difficult to measure.
