Open sylvchev opened 1 year ago
Hi @sylvchev,
Do you have the biomodal implemented somewhere?
Yes, the paper is here, their matlab code is here and my python version is:
def chance_level(nbepoch, nbclass, alpham=0.01):
# nbepoch = length(y);
# nbclass = length(unique(y));
threhold = binom.ppf(1-alpham, nbepoch, 1/nbclass) * 100/nbepoch
return threhold
alpham
is the target p-value (for example 0.01)
MOABB has a very good statistical methodology for experimental analysis and benchmarking algorithms. It could be a nice addition to reinforce these aspects with:
Regarding chance level, we simply use the limit when the number of samples tends to infinity (50% for 2 classes, 25% for 4 classes, etc). This chance level is a crude approximation and could be refined, either using the class sample balance (Better than random: a closer look on BCI results) or by characterizing the class sample distribution (Exceeding chance level by chance)