I am working on the famous ABIDE Autism Datasets. The dataset is very big in the sense that it has more than 1000 subjects containing half of them as autistic and the other half as healthy controls. The dataset is taken from 17 sites across the world and each site used a varying time dimension when recording the subjects fMRI.
I want to use this dataset for a classification task but the only issue is time-varying subjects as features set are fixed to 200 so you can say that I have subjects dimensions like 150 x200, 75 x 200 , 300 x 200... so on. So what are advanced AI or deep learning techniques that I can use to fix this time dimension for every subjects or can anybody suggest some deep learning framework or model that I could use to fix these varying time dimensions across subjects?
I have applied PCA to the time dimension and fixed them to 50 and tried other numbers also but it did not produce good accuracy for classification
I also tried to use only specific time points from every subject like taking only 40 time points from every subject to fix the dimension but again it did not work as definitely filtering some time series data on every subject would lose crucial information.