In neuroscience it is very common to measure the reaction time (RT) of subjects. Based on the RT conclusions can be made about personal working memory capability, IQ etc.
So I have such data, which came from some neuroscience experiment. In this data I have 180 subjects from two groups (90 subjects in each group) for simplicity let say boys and girls, and I have a set of 500 RTs for each subject. The difference between the mean RT of each group is statistically significant (t-test). Now, I would like to construct a classifier which will learn the differences between those two groups. I want the classifier to classify new subjects only based on their RT - and I'm talking specifically about classification of RT.
(A) I'm searching for dimensionality-reduction method which would fit to this case. (
I have tried PCA and it doesn't work well ). Not sure if PCA is the right thing here as there is no difference between the 100th and the 200th RT. Each RT is independent and there is no specific ordering - so there is not any co-variance here that PCA can capture. (right ?)
(B) Which classifier is recommended in such case ?
Does anyone aware of such kind of work in general and specific for RT ? Any information on this would be appreciated.