UC San Diego

In this work, we explore the topic of forecasting the neural time series using machine-learning based techniques on electroencephalography (EEG) data. Forecasting EEG has a number of potential applications in brain-computer interfaces (BCI), such as ahead-of-time event classification, cognitive response prediction, and preemptive intervention therapy. However, previous work in EEG forecasting has failed to accurately predict the time series more than a few steps into the future. Simple linear models lack the capacity to model the high-dimensional dynamics of EEG activity, while complex nonlinear models are difficult to specify and implement. However, recent deep neural networks have effectively modeled high-dimensional systems in a variety of domains. In this work, we hope to bridge the gap between previous work in EEG forecasting and current techniques in deep learning. In particular, we explore forecasting the EEG patterns that occur after the presentation of a time-locked visual stimulus. We implement a deep neural network that extracts features from pre-event data in order to predict single-trial event-locked EEG data. To capture the variation of a single trial, the network constructs the post-event waveform in two parts: 1) generating ongoing neural activity and 2) generating evoked event-related responses. We evaluate our model by forecasting 500 milliseconds of single channel post-event data from a Rapid Serial Visual Presentation (RSVP) task. Our results indicate a significant increase in forecasting performance compared to baseline methods, suggesting that deep neural networks can extract informative features from EEG data in order to generate a prediction of the post-event waveform.