The goal of this project is to study the neurological bases of language using intracranial electroencephalography (iEEG) signals recorded from drug-resistant epilepsy patients. In particular, we aim to address two current clinical challenges. Firstly, we intend to individually identify the brain regions involved in the production and understanding of language, in order to preserve these regions during brain surgery for epilepsy treatment. Secondly, this project also aims to develop novel pattern recognition algorithms that can decode speech from iEEG signals obtained from participants performing language production tasks. The ultimate goal is to evaluate the feasibility of a neuroprosthetic device that could restore oral communication in persons that cannot speak following a neurodegenerative disease or brain damage. For both goals, a series of experimental tasks will be developed in order to thoroughly evaluate language production and comprehension. Furthermore, data derived from these tasks will be analyzed using state-of-the-art multivariate statistical methods and machine learning techniques (e.g., deep learning). In addition to having a social impact, the results of this project will also help in advancing the knowledge about the neural substrates that underpin language production and comprehension.