text: The ms applies an LSTM on ECoG data and studies tranfer between subjects etc. The data includes only few samples per class. The validation procedure to obtain the model accuray is a bit iffy. The ms says: The test data contains 'at least 2 samples per class'. Data of the type analysed is highly dependend, so it is not unclear whether this validation procedure will not provide overoptimistic results. Currently, I do not see evidence for a stable training procedure in the ms. I would be curious also to see a comparison to a k-NN classifier using embedded data to gauge the problem difficulty. Also, the paper does not really decide whether it is a neuroscience contribution or an ML one. If it were a neuroscience contribution, then it would be important to analyse and understand the LSTM representation and to put it into a biological context fig 5B is a first step in this direction. If it where a ML contribution, then there should be a comprehensive analysis that indeed the proposed architecture using the 2 steps is actually doing the right thing, i.e. that the method converges to the truth if more and more data is available. There is also some initial experiments in fig 3A. Currently, I find the paper somewhat unsatisfactory and thus preliminary.