Suppose I have collected the hemodynamic responses of participants when they were performing cognitive tasks (e.g. n-back) using a 16-channel functional near-infrared spectroscopy device. I would like to extract some features of the data and perform machine learning to classify participants (e.g. demented vs. not demented / ASD vs. non-ASD).
Can the oxygenated hemoglobin data in all 16 channels of each participant treated as 16 distinct observations? That is, if I have 3 participants, the sample size = 3 participants x 16 channels = 48. I wonder if that is possible? (the data in each channel may correlate with each others)
And, if I use this way to increase the sample size, will it violate any assumptions of machine learning?