I'm using the OpenBCI cyton/daisy setup, and it doesn't have EOG channels setup by default, also we're not working with huge data sets so we want to concatenate the data from one participant but split it into sets. As such this PR is to:
Added MNE related options to conf
notch filtering
adding an average ref channel
merge PR #75 and #76
Fix Thinker.split() returning unwrapped torch.utils.data.dataset.Subset instead returning a new DN3ataSubSet object which wrapps Subset and includes a get_targets method to allow using the returned training, validating and testing variables inside a BaseProcess.fit method with a balance_method argument
I'm using the OpenBCI cyton/daisy setup, and it doesn't have EOG channels setup by default, also we're not working with huge data sets so we want to concatenate the data from one participant but split it into sets. As such this PR is to:
Thinker.split()returning unwrappedtorch.utils.data.dataset.Subsetinstead returning a newDN3ataSubSetobject which wrapps Subset and includes aget_targetsmethod to allow using the returnedtraining,validatingandtestingvariables inside aBaseProcess.fitmethod with abalance_methodargument