I’m not familiar with this paper, but if they use in_channels=1, you should use your first approach (input dim = [20, 1, 4*200]).
In a lot of cases, you will feed color images into your ConvNet, so that the input will have a dimension of [batch, 3, w, h]. In the next layers the channel dimension is defined by e.g. the number of kernels in your ConvLayers.
However, gray-scale images will also have one single channel, i.e. [batch, 1 ,w, h].