Topic Modeling for Inferring Brain States from Electroencephalography (EEG) Signals

Inferring brain states from EEG signals allows for the management of sleep disorders and brain diseases by providing an insight into the electrophysiological state of the brain. We explore the use of topic modeling – which are popular text processing algorithms – to infer brain states from EEG signals. Latent Dirichlet allocation (LDA) is our preferred topic model because of its mixture-of-mixtures nature and its ability to be trained in an unsupervised manner. First, we present an architecture of a deep convolutional auto-encoder neural network to automatically learn feature representations from EEG signals. The network uses a combination of convolutional and max-pooling layers to achieve reduction in the dimensionality of raw data, and can be trained in an unsupervised manner. We demonstrate an improvement in clustering EEG signals into sleep stages with the LDA topic model using features derived from the auto-encoder, compared to standard manually extracted EEG features. Next, we address the issue of modeling continuous domain data using topic modeling. In the LDA topic model, topics are modeled as discrete distributions over a finite vocabulary of words. Modeling data spanning a continuous domain with the LDA requires discrete approximations of the continuous data, which can lead to loss of information and may not represent the true structure of the underlying data. We present the GMM-LDA topic model, where topics are represented using Gaussian mixture models (GMMs), which are multi-modal distributions spanning a continuous domain. We present results demonstrating superior clustering performance in clustering EEG data into sleep stages using the GMM-LDA topic model compared to the standard LDA and other clustering algorithms. Finally, we explore a set of features that can be potentially used with topic modeling to infer brain states corresponding to brain injury in mice. Spectral, entropy and moment related features are extracted from EEG signals recorded from mice with artificially induced brain injury. We present an analysis on the relative importance of these features using bagged decision trees, and demonstrate that a combination of these features can potentially be used to track the progression of brain injury and also to predict recovery from brain injury in mice.