Forecasting EEG time series with WaveNet

Objective: Forecasting electroencephalography (EEG) signals, i.e., estimating future values of the time series based on the past ones, is essential in many real-time EEG-based applications, such as brain-computer interfaces and closed-loop brain stimulation. As these applications are becoming more and more common, the importance of a good prediction model has increased. Previously, mainly the autoregressive model (AR) has been employed for this task - however, its prediction accuracy tends to fade quickly as multiple steps are predicted. We aim to improve on this by applying deep learning to make robust long-range forecasts. Methods: We applied the deep neural network model WaveNet to forecast resting-state EEG in theta- (4-7.5 Hz) and alpha-frequency (8-13 Hz) bands. We also compared WaveNet to the AR model, which has previously been widely used in real-time EEG applications. Results: WaveNet reliably forecasted EEG signals in both theta and alpha frequencies. It outperformed the AR model in estimating the signal amplitude and phase. Conclusion: We demonstrate for the first time that deep learning can be utilized to forecast resting-state EEG time series over 100 ms ahead. Significance: In the future, the developed model can enhance the real-time estimation of brain states in brain-computer interfaces and brain stimulation protocols. It may also be useful for answering neuroscientific questions and for diagnostic purposes. ### Competing Interest Statement The authors have declared no competing interest.