This thesis presents a novel neurofeedback system for mu-rhythm modulation using an adversarial deep learning approach. The goal was to train subjects to modulate the mu-rhythm in their brain activity and to investigate the usability of this system for reflex modulation experiments. Two EEG classifiers were implemented: a Rest vs. Motor Imagery (RestMI) model and a Motor Imagery vs. Motor Movement (MIMM) discriminator. Five healthy subjects participated in five sessions of BCI training followed by a reflex assessment. During the reflex assessment the subjects had to hold a constant flexion in their wrist in order to provoke mechanical reflexes, which introduced an extra challenge for the classifiers. The RestMI model achieved a mean classification accuracy of 0.73 in the first two sessions, however performance decreased when trials with wrist flexion were introduced. The MIMM model showed a low online performance during early sessions, indicating subjects could deceive the discriminator. The reflex assessment showed mixed results, with indication of modulation of the long latency response. These findings suggest adversarial DL models can support specific mu-rhythm training in some subjects, although further work is needed with a larger sample size and more task-specific training sessions.