Assessment criteria for MEG/EEG cortical patch tests.
To validate newly developed methods or implemented software for magnetoencephalography/electroencephalography (MEG/EEG) source localization problems, many researchers have used human skull phantom experiments or artificially constructed forward data sets. Between the two methods, the use of an artificial data set constructed with forward calculation attains superiority over the use of a human skull phantom in that it is simple to implement, adjust and control various conditions. Nowadays, for the forward calculation, especially for the cortically distributed source models, generating artificial activation patches on a brain cortical surface has been popularized instead of activating some point dipole sources. However, no well-established assessment criterion to validate the reconstructed results quantitatively has yet been introduced. In this paper, we suggest some assessment criteria to compare and validate the various MEG/EEG source localization techniques or implemented software applied to the cortically distributed source model. Four different criteria can be used to measure accuracy, degrees of focalization, noise-robustness, existence of spurious sources and so on. To verify the usefulness of the proposed criteria, four different results from two different noise conditions and two different reconstruction techniques were compared for several patches. The simulated results show that the new criteria can provide us with a reliable index to validate the MEG/EEG source localization techniques.
Im
Chang-Hwan
CH
ENG420-040, School of Electrical Engineering, Seoul National University, Shillim-dong, Kwanak-gu, Seoul 151-742, Korea. ichism@elecmech.snu.ac.kr
An
Kwang-Ok
KO
Jung
Hyun-Kyo
HK
Kwon
Hyukchan
H
Lee
Yong-Ho
YH
D000465:Algorithms; D002540:Cerebral Cortex / Q000502:physiology*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009431:Neural Conduction / Q000502:physiology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012986:Software Validation; D013269:Stochastic Processes