Brain activity was recorded using the Unicorn Hybrid Black device, which collects the user's EEG data. The signals were then analyzed and processed in Python, enabling the recognition of the type of limb movement the user was thinking about at a given moment. After classification, the data was transmitted to target devices using appropriate communication protocols.

According to the team, the biggest challenge proved to be both the volume of data and its complex processing. Initially, this posed a significant limitation, but after testing various methods and gaining access to greater computational power, they managed to overcome these difficulties.