Task performance metrics, statistical, and classification methods
This protocol is extracted from research article:
Assessing bimanual motor skills with optical neuroimaging
Sci Adv, Oct 3, 2018; DOI: 10.1126/sciadv.aat3807

The FLS scores were determined using the standardized FLS scoring metric formulation for the PC task based on time and error. This formulation is intellectual property–protected and was obtained with consent under a nondisclosure agreement from the FLS Committee, and hence, its details cannot be reported in this paper. Descriptive and inferential statistics were performed using SPSS (IBM Inc.). Two-sample t tests were used to determine statistically significant differences in functional activation between two groups. All box plots display median values (red bar) along with SDs. A confidence level of 95% was selected as the minimum required to reject the null hypothesis.

LDA was used to classify the populations on the basis of their FLS scores and functional brain activation metrics. Before the analysis of LDA, all recorded metrics were first normalized, that is, the sample mean and variance are 0 and 1. LDA determines the optimal vector v such that the projected metrics of two classes (for example, Novice and Expert surgeons) in the v direction have the highest separation between the classes with the lowest variance for each class (60). The resulting LDA scores were objectively compared for each class, and the degree of separation was objectively quantified as MCEs.

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