The mathematical / statistical linear model, $y_{ij} = \mu + \rho_i + \tau_j + \epsilon_{ij},$ where $\mu$ is the population mean, $\rho_i$ is the main effect of subject, $\tau_j$ is the main effect of treatment, $\epsilon_{ij}$ is the independent error (link, p. 2), describes a single-factor repeated measures design.
It is said that for the data collected in such a design we have to operate with the residuals calculated accordingly to the formula A:
$$\hat\epsilon_{ij} = Y_{ij} - \bar Y_{i.} - \bar Y_{.j} + \bar Y.$$

The formula B, on wiki is $$\hat\epsilon_i = Y_i - \bar Y.$$

My question is:
what formula is used to correctly calculate residuals in the model describing a single-factor repeated measures design to check assumptions (multivariate normality, homoscedasticity, sphericity, linearity)?

Thank you.

P.S.
D.C. Montgomery in "Design and Analysis of Experiments" provides the same model expression but with slightly different notations.