Intracranial electroencephalographic (iEEG) signals are typically captured using matrices with a high density of electrodes. This makes compressing these signals a valid objective for reducing memory usage in storage and transmission. Among the available compression methods, generative adversarial networks (GANs) have not been used. GANs are primarily associated with image generation and image-related applications. They can capture the probability distribution of variables in a dataset. Compression algorithms have minimally explored this characteristic, especially when applied to time series. We present an algorithm for multichannel signal compression based on a GAN, and we prove its efficacy on a dataset of intracranial electroencephalography (iEEG) signals. The compression method modifies the Backpropagation GAN (BPGAN) algorithm to work with signals instead of images. To get around the difficulties introduced by the peculiarities of the specific application, we propose an algorithm that separates channels of the iEEG signal into groups with high correlation and synchronization. The algorithm compresses groups separately by selecting a latent representation for each signal window among a finite set of possible representations. To validate the results, we decided to compare the results of an epilepsy seizure recognition experiment on the original and reconstructed signal. This approach presents encouraging results regarding compression ratio despite the introduction of some artifacts in the reconstruction. This could open for future improvements in applying GANs in compressing signals by increasing the reconstruction quality while keeping the high compression ratio.