  Recent development in brain-machine interface technology has made seizure
prediction possible. However, the communication of large volume of
electrophysiological signals between sensors and processing apparatus and
related computation become two major bottlenecks for seizure prediction systems
due to the constrained bandwidth and limited computation resource, especially
for wearable and implantable medical devices. Although compressive sensing (CS)
can be adopted to compress the signals to reduce communication bandwidth
requirement, it needs a complex reconstruction procedure before the signal can
be used for seizure prediction. In this paper, we propose C$^2$SP-Net, to
jointly solve compression, prediction, and reconstruction with a single neural
network. A plug-and-play in-sensor compression matrix is constructed to reduce
transmission bandwidth requirement. The compressed signal can be used for
seizure prediction without additional reconstruction steps. Reconstruction of
the original signal can also be carried out in high fidelity. Prediction
accuracy, sensitivity, false prediction rate, and reconstruction quality of the
proposed framework are evaluated under various compression ratios. The
experimental results illustrate that our model outperforms the competitive
state-of-the-art baselines by a large margin in prediction accuracy. In
particular, our proposed method produces an average loss of 0.35 % in
prediction accuracy with a compression ratio ranging from 1/2 to 1/16.
