Naive random subspace ensemble with linear classifiers for real-time classification of fMRI data

Functional magnetic resonance imaging (fMRI) provides a spatially accurate measure of brain activity. Real-time classification allows the use of fMRI in neurofeedback experiments. With limited labelled data available, a fixed pre-trained classifier may be inaccurate. We propose that streaming fMRI data may be classified using a classifier ensemble which is updated through naive labelling. Naive labelling is a protocol where in the absence of ground truth, updates are carried out using the label assigned by the classifier. We perform experiments on three fMRI datasets to demonstrate that naive labelling is able to improve upon a pre-trained initial classifier.

► fMRI data is classified using online linear classifiers. ► A random subspace ensemble is used. ► fMRI data labels may not always be available. ► Predicted labels are used to update the ensemble member classifiers (naïve labelling).