This thesis presents an algorithm to compressed multichannel signals which makes use of a Generative Adversarial Network for the first time. It is a modification of BPGAN by implementing LSTM neural networks instead of convolutional neural networks. BPGAN considers to select an optimal compressed representation that is also an element of a finite set of possible representations which allows a direct control of the value of compression ratio by selecting the cardinality of this set. The algorithm is tested on an intracranial electroencephalographic signal dataset, this has required some attentions because of the limited amount of information in literature about compressing iEEG and also because of the specific characteristics of this kind of signal.