## 4 Discussion and Conclusions  
In this paper, a genetic algorithm was used to obtain one column Jansen-Rit model parameters. A spectral similarity function was minimized to generate most optimal parameters for a specific individual. This method proved to be repeatable and accurate, but quite computationally expensive. However, given the convergence curve in Figure 6, the best value was obtained as a best score already in about the 30 th population. That means that algorithm could potentially give results faster. On the other hand, convergence of stochastic algorithms tends to depend strongly on initial population. In future works, it would be beneficial to conduct repeatability studies on the proposed method with limited maximal population. Results of conducted repeatability studies with the proposed maximal population suggest that the algorithm does not get stuck in local minima. Results of repeatability and accuracy can point out which parameters have the greatest contribution to the value of the minimized function, and, therefore, in future studies, which parameters could be expected to constitute the most informative signal features. Hence: \(A\) and \(B\) (the maximal post-synaptic potential amplitudes) have rather high accuracy compared to the other parameters. ICC value of A, however, is comparatively low. Nonetheless, by itself, the intraclass correlation of 0.897 can be interpreted as good [31]. C, so the numbers of synapses between neuron populations, proved to be the most determinative of the minimized function, yielding ICC at 0.978 and the accuracy of 0.853. It is not surprising, as this parameter is known to have biggest influence on the shape of the modeled signal [10]. Parameters describing the activation function gave mixed results. On one hand, \(v0\) and r turned out to be quite descriptive. On the other hand, ICC and accuracy values of the \(e0\) parameter suggest that it is not as informative. It may be due to the quite narrow sought range and should be studied in future research. Parameters characterizing the noise signal delivered to the model also proved to be very descriptive, wielding highest accuracy and very high ICC. Parameter \(p\) was extensively studied in [10], providing deeper insight into its influence on the whole model. Values of the parameters obtained for the real signal significantly differ from the original works and are close to defined ranges' extremes. That could suggest that those ranges could be further widened in future works. Results obtained in this paper show that the genetic algorithm works well in a J-R model parameter obtaining task. The defined function can be effectively minimized. Due to the stochastic character of this type of algorithm, the main disadvantage of the proposed method is the computational cost--it is certainly not a fast way to obtain model parameters. However, there is already an online method of parameter obtainment [23; 24; 25]. State observers proposed in those works predict model parameters dynamically, i.e., in a specific timestamp. The method proposed in this paper enables to obtain parameters for arbitrarily long time periods. Furthermore, this research was conducted on a personal computer. Utilizing some sort for computation cluster would significantly reduce the computation time. Utilization of J-R model parameters as a classifier input should be checked in future studies. The parameters should be obtained for an already classified set of signals and used to train some classification algorithm. Conceptualization, A.L. S.P. ; methodology, A.L. S.P. software, A.L. ; validation, S.P. ; formal analysis, A.L., S.P. ; investigation, A.L., S.P. ; resources, A.L. ; data curation, A.L. ; writing--original draft preparation, A.L., S.P. ; writing--review and editing, A.L., S.P. ; visualization, A.L. ; supervision, S.P. ; project administration, A.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.