How to extract features from the encoded layer of an autoencoder?
You are using a dense neural network layer to do encoding. This layer does a linear combination of the input layers + specified non-linearity operation on the input.

Important to note that auto-encoders can be used for feature extraction and not feature selection. It will take information represented in the original space and transform it to another space. The compression happens because there's some redundancy in the input representation for this specific task, the transformation removes that redundancy. Original features are lost, you have features in the new space.

Which input features are being used by the encoder? Answer is all of them. For how exactly are they used? Answer is you can check the weights assigned by the neural network for the input to Dense layer transformation to give you some idea. You can probably build some intuition based on the weights assigned (example: output feature 1 is built by giving high weight to input feature 2 &amp; 3. So encoder combined feature 2 and 3 into single feature) . But there's a non-linearity (ReLu) involved so there's no simple linear combination of inputs.

If your aim is to get qualitative understanding of how features can be combined, you can use a simpler method like Principal Component Analysis. The factor loadings given in PCA method's output tell you how the input features are combined. If the aim is to find most efficient feature transformation for accuracy, neural network based encoder is useful. But you loose interpretability of the feature extraction/transformation somewhat.