Convolutional Neural Network not learning EEG data

Question

I have trained a simple CNN (using Python + Lasagne) for a 2-class EEG classification problem, however, the network doesn't seem to learn. loss does not drop over epochs and classification accuracy doesn't drop from random guessing (50%):

Questions

1. Is there anything wrong with the code that is causing this?
2. Is there a better (more correct?) way to handle EEG data?

EEG setup

Data is collected from participants completing a total of 1044 EEG trials. Each trial lasts 2 seconds (512 time samples), has 64 channels of EEG data, and labelled 0/1. All trials have been shuffled so as to not learn on one set of participants and test on another.

The goal is to predict the label of a trial after being given the 64x512 matrix of raw EEG data

The raw input data (which I can't show here as its part of a research project) has a shape of (1044, 1, 64, 512)

train/validation/test splits are then created at 60/20/20%

With such a small dataset I would have thought overfitting would be a problem, but training loss doesn't seem to reflect that

Code

Network architecture:

def build_cnn(input_var=None):
    l_in = InputLayer(shape=(None, 1, 64, 512), input_var=input_var)

    l_conv1 = Conv2DLayer(incoming = l_in, num_filters = 32, filter_size = (1, 3),
                        stride = 1, pad = 'same', W = lasagne.init.Normal(std = 0.02),
                        nonlinearity = lasagne.nonlinearities.rectify)

    l_pool1 = Pool2DLayer(incoming = l_conv1, pool_size = (1, 2), stride = (2, 2))

    l_fc = lasagne.layers.DenseLayer(
            lasagne.layers.dropout(l_pool1, p=.5),
            num_units=256,
            nonlinearity=lasagne.nonlinearities.rectify)

    l_out = lasagne.layers.DenseLayer(
            lasagne.layers.dropout(l_fc, p=.5),
            num_units=2,
            nonlinearity=lasagne.nonlinearities.softmax)

    return l_out

Note: I have tried adding more conv/pool layers as I thought the network wasnt deep enough to learn the categories but 1) this doesn't change the outcome I mentioned above and 2) I've seen other EEG classification code where a simple 1 conv layer network can get above random chance

Helper for creating mini batches:

def iterate_minibatches(inputs, targets, batchsize, shuffle=False):
    assert len(inputs) == len(targets)
    if shuffle:
        indices = np.arange(len(inputs))
        np.random.shuffle(indices)
    for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
        if shuffle:
            excerpt = indices[start_idx:start_idx + batchsize]
        else:
            excerpt = slice(start_idx, start_idx + batchsize)
        yield inputs[excerpt], targets[excerpt]

Running the model:

def main(model='cnn', batch_size=500, num_epochs=500):
    input_var = T.tensor4('inputs')
    target_var = T.ivector('targets')

    network = build_cnn(input_var)

    prediction = lasagne.layers.get_output(network)
    loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
    loss = loss.mean()

    train_acc = T.mean(T.eq(T.argmax(prediction, axis=1), target_var),
                      dtype=theano.config.floatX)

    params = lasagne.layers.get_all_params(network, trainable=True)

    updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.01)

    test_prediction = lasagne.layers.get_output(network, deterministic=True)
    test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
                                                            target_var)
    test_loss = test_loss.mean()

    test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),
                      dtype=theano.config.floatX)

    train_fn = theano.function([input_var, target_var], [loss, train_acc], updates=updates)

    val_fn = theano.function([input_var, target_var], [test_loss, test_acc])

    print("Starting training...")

    for epoch in range(num_epochs):
        # full pass over the training data:
        train_err = 0
        train_acc = 0
        train_batches = 0
        start_time = time.time()
        for batch in iterate_minibatches(train_data, train_labels, batch_size, shuffle=True):
            inputs, targets = batch
            err, acc = train_fn(inputs, targets)
            train_err += err
            train_acc += acc
            train_batches += 1

        # full pass over the validation data:
        val_err = 0
        val_acc = 0
        val_batches = 0
        for batch in iterate_minibatches(val_data, val_labels, batch_size, shuffle=False):
            inputs, targets = batch
            err, acc = val_fn(inputs, targets)
            val_err += err
            val_acc += acc
            val_batches += 1

    # After training, compute the test predictions/error:
    test_err = 0
    test_acc = 0
    test_batches = 0
    for batch in iterate_minibatches(test_data, test_labels, batch_size, shuffle=False):
        inputs, targets = batch
        err, acc = val_fn(inputs, targets)
        test_err += err
        test_acc += acc
        test_batches += 1

# Run the model
main(batch_size=5, num_epochs=30)

Answer 1

I had the same problem when I used TensorFlow to build a self driving car. The training error for my neural nets bounced around forever and never converged on a minimum. As a sanity check I couldn't even intentionally get my models to overfit, so I knew something was definitely wrong. What worked for me was scaling my inputs. My inputs were pixel color channels between 0 and 255, so I divided all values by 255. From that point onward, my model training (and validation) error hit a minimum as expected and stopped bouncing around. I was surprised how big of a difference it made. I can't guarantee it will work for your case, but it's definitely worth trying, since it's easy to implement.

Answer 2

There are a lot of possible reasons why your setup might not work. However, one very good start is to try to overfit your model on a very small subsample of your entire dataset just to see if the problem is in the code.

Answer 3

1.Your input layer seems off, the first dimension is for channels, please try, with the data formatted correctly :

l_in = InputLayer(shape=(None, 64, 512, 1 ), input_var=input_var)

A more clean way would be to replace the conv2dlayer by a conv1dLayer, which is what you are replicating.

2.There is no correct way to handle eeg. But people often also use spectrograms and feature extraction