I'd love to do a survey to see how people respond to these choices; my guess is many would opt for a) and few would opt for c). Yet in this situation, the likelihood of the result being a false positive is very high – much higher than many people realise.
Another feature of ERP research is that there is flexibility in how electrodes are handled in an ANOVA design: since there is symmetry in electrode placement, it is not uncommon to treat hemisphere as one factor, and electrode site as another. The alternative is just to treat electrode as a repeated measure. This is not a neutral choice: the chances of spurious findings is greater if one adopts the first approach, simply because it adds a factor to the analysis, plus all the interactions with that factor.
PS. 2nd July 2013
There's remarkably little coverage of this issue in statistics texts, but Mark Baxter pointed me to a 1996 manual for SYSTAT that does explain it clearly. See: http://www.slideshare.net/deevybishop/multiway-anova-and-spurious-results-syt
The authors noted "Some authors devote entire chapters to fine distinctions between multiple comparison procedures and then illustrate them within a multi-factorial design not corrected for the experiment-wise error rate."
They recommend doing a Q-Q plot to see if the distribution of p-values is different from expectation, and using Bonferroni correction to guard against type I error.
They also note that the different outputs from an ANOVA are not independent if they are based on the same mean squares denominator, a point that is discussed here:
Hurlburt, R. T., & Spiegel, D. K. (1976). Dependence of F Ratios Sharing a Common Denominator Mean Square. The American Statistician, 30(2), 74-78. doi: 10.2307/2683798
These authors conclude (p 76)
It is important to realize that the appearance of two significant F ratios sharing the same denominator should decrease one's confidence in rejecting either of the null hypotheses. Under the null hypothesis, significance can be attained either by the numerator mean square being "unusually" large, or by the denominator mean square being "unusually" small. When the denominator is small, all F ratios sharing that denominator are more likely to be significant. Thus when two F ratios with a common denominator mean square are both significant, one should realize that both significances may be the result of unusually small error mean squares. This is especially true when the numerator degrees of freedom are not small compared' to the denominator degrees of freedom.