The recording of intracranial electroencephalogram (iEEG) signals is essential in neuroscience but produces large amounts of data due to their high temporal resolution and dimensionality. Efficient compression methods are therefore required, yet the variability of iEEG across subjects and channels makes this task particularly challenging. This thesis builds on a previous study that explored Generative Adversarial Networks (GANs) for iEEG compression which introduces an encoder within the architecture to produce latent representations of the signals. Training follows a two-phase procedure: joint training of encoder and GAN, and optimization-quantization of the latent space while refining the GAN. We introduced a clustering strategy as a preprocessing step.