In the past years, it has been proven that the use of AI algorithms is useful to improve the control on Brain-Machine Interfaces (BMI). Through the search for patterns in the neural signal by Machine Learning (ML) algorithms, it was possible to derive information about the brain activity, leading to a greatly improved accuracy in neural decoding. BMIs were proposed that allowed people to compose sentences by selecting letters on a virtual keyboard, others that allowed people to control chronic pain through stimulation of the brain in a virtual environment, and others that allowed control of a robotic arm. However, it has been proven that neural signals change over time, mainly due to neuroplasticity, but also due to inflammatory processes and the displacement of electrode microarrays in the case of intracortical signal retrieval (ECoG).