Multiple aspects of these new scientific ventures with EEG recordings may prove useful for clinical purposes. With the advent of big data and the successful application of machine learning techniques, EEG research can start measuring up with other imaging modalities to perform disease classification and treatment outcomes. These predictive techniques are maturing quickly (Janssen et al., 2018). In fMRI research, imaging the activity of the brain pretreatment can successfully predict electroconvulsive treatment (ECT) outcome for otherwise treatment‐refractory depressed patients (Waarde et al., 2015). Bridging such findings to EEG research will require novel designs in artificial neural networks tuned to the specific spatio‐temporal aspects of EEG oscillations (Schirrmeister et al., 2017). These have so far largely been developed for detecting epileptic seizures, sleep staging, and brain computer interfacing (BCI (Ding et al., 2015)). We foresee an expansion of such models to many other areas of behavioral (dys)function.
