  Objective. Deep neural networks (DNNs) have shown unprecedented success in
various brain-machine interface applications such as epileptic seizure
prediction. However, existing approaches typically train models in a
patient-specific fashion due to the highly personalized characteristics of
epileptic signals. Therefore, only a limited number of labeled recordings from
each subject can be used for training. As a consequence, current DNN based
methods demonstrate poor generalization ability to some extent due to the
insufficiency of training data. On the other hand, patient-independent models
attempt to utilize more patient data to train a universal model for all
patients by pooling patient data together. Despite different techniques
applied, results show that patient-independent models perform worse than
patient-specific models due to high individual variation across patients. A
substantial gap thus exists between patient-specific and patient-independent
models. Approach. In this paper, we propose a novel training scheme based on
knowledge distillation which makes use of a large amount of data from multiple
subjects. It first distills informative features from signals of all available
subjects with a pre-trained general model. A patient-specific model can then be
obtained with the help of distilled knowledge and additional personalized data.
Main results. Four state-of-the-art seizure prediction methods are trained on
the Children's Hospital of Boston-MIT sEEG database with our proposed scheme.
The resulting accuracy, sensitivity, and false prediction rate show that our
proposed training scheme consistently improves the prediction performance of
state-of-the-art methods by a large margin. Significance. The proposed training
scheme significantly improves the performance of patient-specific seizure
predictors and bridges the gap between patient-specific and patient-independent
predictors.
