![](bg9.png)
reconstruction, however, it is worth noticing that the
solutions by sLORETA are smooth (Pascual-Marqui,
2002), while the other hand, MSP present more sparse
solutions (L
´
opez et al., 2014; Friston et al., 2008).
Therefore in future works, we will consider the use
of error measurements that involve the temporal evo-
lution of the reconstructed sources and the sparseness
of the solutions.
The pipeline presented considers a basic filter
stage using band-pass filters with the intention to fo-
cus on the partial brain models to source activity
reconstruction. However, several studies (Mu
˜
noz-
Guti
´
errez et al., 2018; Hansen et al., 2019) have
shown that the using of advanced techniques for fre-
quency decomposition like empirical mode decompo-
sition EMD, multivariate EMD, noise assisted EMD,
and wavelets can offer a solution for unmixing the
source activity improving the brain mapping algo-
rithms. Those techniques will be studied on partial
brain models in future publications.
AUTHOR CONTRIBUTIONS
This part was intentionally removed for reviewing
purposes All the authors conceived and designed the
experiments. AFS performed the experiments. All
the authors analyzed the data, wrote and refined the
article.
ACKNOWLEDGMENT
This part was intentionally removed for reviewing
purposes This work was supported by the Norwegian
University of Science and Technology NTNU, project
”David and Goliath: single-channel EEG unravels its
power through adaptive signal analysis”.
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