I would appreciate your comments/help about a strategy I am applying in one of my analysis. In short, my case is:1) My data have biological origin, collected in a period of 120s, from a subject receiving, each time, one of possible three stimuli (response label 1 to 3), in a random manner, one stimulus per second (trial). Sampling  frequency is 256 Hz and 61 different sensors (input variables). So, my  dataset has 120x256 rows and 62 columns (1 response label + 61 input  variables);2) My goal is to identify if there is an underlying pattern for each stimulus. For that, I would like to use deep learning neural networks to test my hypothesis, but not in a conventional way (to predict the stimulus from a single observation/row).3) My approach is to divide the whole dataset, after shuffling per row (avoiding any time bias), in training and validation sets (50/50) and then to run the deep learning algorithm. The division does not segregate trial events (120), so each training/validation sets should contain data (rows) from the same trial (but never the same row). If there is a dominant pattern per stimulus, the validation confusion matrix error should be low. If there is a dominant pattern per trial, the validation confusion matrix error should be high. So, the validation confusion matrix error is my indicator of the presence of a hidden pattern per stimulus;I would appreciate any input you could provide me regarding the validity of my logic. I would like to emphasize that I am not trying to predict the stimulus based on row inputs.Thanks.
Heterodox use of Deep Learning to find hidden patterns
machine learning;deep learning