WithinSessionEvaluation should behave differently for ERP-based paradigms

In many ERP-based datasets, the epochs aligned to a stimulus will overlap to a large extent, e.g. when considering a 0.5 second window after the stimulus, but the time between the onset of stimuli (SOA) was only 100ms. Consider the following example with 5 epochs, where T denotes train and V denotes validation.

onset T1   T2   V3   T4   T5

time   0  100  200  300  400

V3 is picked for validation purposes, however in 500ms epochs, the last 300ms of T1 contain the first 300ms of V3 and the first 400ms of T4 contain the last 400ms of V3, etc.

Note that this is usually not an issue in Motor Imagery / SSVEP as these epochs seldomly overlap.

In my fork of moabb I perform train / validation splits only on borders between runs (usually different EEG files and concluded short sections of recordings). However, some datasets do not have run-information, here we could either detect the borders based on event times, or if events only have sequence but no time information, just pick continuous sections of recording and omit X epochs at the border (according to SOA + epoch size).
The TimeSeriesSplit of sklearn could be an alternative but here each training-fold has different sizes. In my opinion for ERP/P300 paradigms it is not useful to train a classifier in WithinSessionEvaluation on e.g. 80% of the data, as these results are mostly useless for practical BCI usage.

I dont know whether to create a new WithinSessionEvaluationChronological or change the default behaviour of WithinSessionEvaluation (In my own fork of moabb I do the latter).

Begin with WithinSessionEvaluation and compare the proposed TimeSeriesSplit approach with splits at run or recording boundaries. Resolve whether ERP paradigms should change the default or use a separate evaluation, including cases without run or event-time information; done means an agreed chronological split policy that prevents overlapping train and validation epochs.