Using an autoencoder to mimic independent component analysis?

I'm trying to use autoencoders in keras to create a linear transformation similar to independent component analysis (ICA) (using this to denoise electroencephalographic data, time series of 64x100000 pts). While the autoencoder does a good job of re-creating the input using a smaller number of neurons in the hidden layers, there's no structure to the weights in the hidden layers, i.e., it doesn't seem to isolate structure in the data, it just mixes everything up in the compressed layers. For example, ICA is able to isolate things like heart-beats, eye-blinks, and brain activity, but the reconstructed time-series in the hidden layers are just linear mixtures of all of these different signals.

Does anyone know how to force the time series constructed in the hidden layers to be temporally independent? I'm thinking I'll have to create some kind of custom regularizer, but I have no idea how to go about this.

Here's my keras code:

import numpy as np
import scipy as scipy
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout
from keras.layers import BatchNormalization
import matplotlib.pyplot as plt
from keras import regularizers
from keras.constraints import max_norm, non_neg, unit_norm
from keras import backend as K

raw = raw['raw'];

#acts = acts['acts'];

model = Sequential()