  Epilepsy is a chronic neurological disorder affecting 1\% of people
worldwide, deep learning (DL) algorithms-based electroencephalograph (EEG)
analysis provides the possibility for accurate epileptic seizure (ES)
prediction, thereby benefiting patients suffering from epilepsy. To identify
the preictal region that precedes the onset of seizure, a large number of
annotated EEG signals are required to train DL algorithms. However, the
scarcity of seizure onsets leads to significant insufficiency of data for
training the DL algorithms. To overcome this data insufficiency, in this paper,
we propose a preictal artificial signal synthesis algorithm based on a
generative adversarial network to generate synthetic multichannel EEG preictal
samples. A high-quality single-channel architecture, determined by visual and
statistical evaluations, is used to train the generators of multichannel
samples. The effectiveness of the synthetic samples is evaluated by comparing
the ES prediction performances without and with synthetic preictal sample
augmentation. The leave-one-seizure-out cross validation ES prediction accuracy
and corresponding area under the receiver operating characteristic curve
evaluation improve from 73.0\% and 0.676 to 78.0\% and 0.704 by 10$\times$
synthetic sample augmentation, respectively. The obtained results indicate that
synthetic preictal samples are effective for enhancing ES prediction
performance.
