EEG parametric leadfield computation - On all ROI elements¶
The computation of a parametric solution for the EEG leadfield matrix takes almost the same parameters as the single problem.
The first step is to load the finite element model created before.
from shamo import FEM model = FEM.load("../../derivatives/fem_from_labels/fem_from_labels.json")
Next, we import the
ProbParamEEGLeadfield class and create an instance of it.
from shamo.eeg import ProbParamEEGLeadfield problem = ProbParamEEGLeadfield()
As for the single problem, we must set the electrical conductivity of the tissues but this time, we must provide probability distributions. If a parameter is fixed, the
DistConstant can be used. Otherwise, we can pick from the following probability laws:
For the sake of this example, we only use uniform distributions and define the ranges with the values reported in 1.
from shamo import DistUniform problem.sigmas.set("scalp", DistUniform(0.137, 2.1)) problem.sigmas.set("gm", DistUniform(0.06, 2.47)) problem.sigmas.set("wm", DistUniform(0.0646, 0.81))
The electrodes and the regions of interest are set as for the
problem.markers.adds(["NZ", "LeftEar", "RightEar"])
Finally, we can solve the problem to generate n_evals sub-solutions. The method parameter determines how the solutions are solved:
“sequential” means each solution is computed one at a time.
“multiprocessing” means n_proc solutions are computed in parallel on the same computing node.
“jobs” means a python script is generated for every sub-solution. Those scripts can be run in any way we like, on a HPC unit or on the computer. If this solution is chosen, the
finalize()method must be called after all the sub-solutions are generated.
solution = problem.solve("parametric_ssp-elems", "../../derivatives/eeg_leadfield", model, n_evals=4, method="multiprocessing", n_proc=4)
We now have multiple sub-solutions accessible with a single parametric solution. To really use the power of those results, we still have to generate a surrogate model.
Hannah McCann, Giampaolo Pisano, and Leandro Beltrachini. Variation in Reported Human Head Tissue Electrical Conductivity Values. Brain Topography, 32(5):825–858, September 2019. URL: http://link.springer.com/10.1007/s10548-019-00710-2, doi:10.1007/s10548-019-00710-2.