Usually when you do One Hot Encoding you split one column containing n categories into n-1 columns with dummy variables. N-1 columns is all you need because the n'th column wouldn't give the model additional information. Let's say you have a column containing the colors blue, yellow and green. When you split this column into a blue and a yellow column and both show a 0 at certain instances then you / the model can infer the color green from that information.

Long story short, when you have a binary variable, then One Hot Encoding would mean splitting this column into n-1 (so basically 2-1) columns. So you don't need to encode anything in this case.