Regrettably, most of the big difficulties have not been tackled by this revision.
As I mentioned in my former critique, I deem it unlikely that all those issues can be solved simply by a several extra paragraphs. Instead there are however some fundamental worries with the experimental structure and, most critically, with the assessment. This usually means the potent conclusions set forward by this manuscript are not warranted and I can not approve the manuscript in this type. The authors have clarified several of the issues I elevated in my previous evaluation. Sadly, most of the key problems have not been resolved by this revision.
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As I mentioned in my prior evaluate, I deem it unlikely that all people difficulties can be solved basically by a couple of included paragraphs. Rather there are even now some basic problems with the experimental structure and, most critically, with the assessment.
This signifies the solid conclusions put forward by this manuscript are not warranted and I are unable to approve the manuscript in this kind. The best problem is that when I adopted the description of the methods in the preceding version it was feasible to decode, with almost fantastic precision, any arbitrary stimulus labels I selected. See https://doi. org/ten. 6084/m9. figshare. 1167456 for examples of this reanalysis. Regardless of no matter whether we pretend that the real stimulus appeared at a later time or was continuously alternating between sign and silence, the decoding is always close to fantastic. This is an indicator that the decoding has absolutely nothing to do with the genuine stimulus listened to by the Sender but is opportunistically exploiting https://paytowritepaper.io/ some other functions in the info. The manage examination the authors carried out, reversing the stimulus labels, cannot deal with this challenge for the reason that it suffers from the exact exact dilemma.
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In essence, what the classifier is presumably utilizing is the time that has passed considering that the recording started off. The motive for this is presumably that the authors applied non-unbiased information for instruction and screening.
Assuming I have an understanding of the right way (see place three), random sampling a single fifty percent of details samples from an EEG trace are not independent info . Repeating the examination 5 moments – the handle examination the authors performed – is not an sufficient way to handle this issue.
Randomly deciding upon samples from a time series that contains slow alterations (these kinds of as the sluggish wave activity that presumably dominates these recordings beneath these situations) will inevitably contain sturdy temporal correlations. See TemporalCorrelations. jpg in https://doi. org/10. 6084/m9. figshare. 1185723 for 2d density histograms and a correlation matrix demonstrating this. While the revised methods area supplies additional detail now, it however is unclear about specifically what data ended up utilised. Conventional classification assessment report what knowledge characteristics (usual columns in the knowledge matrix) and what observations (normal rows) were being applied. Anything at all could be a function but ordinarily this may possibly be the distinct EEG channels or fMRI voxels etc.
Observations are typically time factors. In this article I believe the authors transformed the uncooked samples into a distinct area employing principal element analysis. It is not said if the dimensionality was diminished employing the eigenvalues. Either way, I suppose the information samples (gathered at 128 Hz) ended up then employed as observations and the EEG channels reworked by PCA were being employed as functions.