2.4. Brain-Computer Interface
MP
The system developed here is a novel MI-BCI [The EEG signal acquisition is achieved using the OpenViBE platform, with an EEG cap used to acquire brain signals through Ag/AgCl electrodes at 8 locations (FC1, FC2, C3, C4, Cz, CP1, CP2, and Pz) positioned according to the International 10–20 system. The A1 Ground (GND) and A2 reference (REF) electrodes are placed on the left and right ears, respectively. The acquired EEG signals are then processed and classified to recognize MI patterns related to pedaling movements. The MI classification is an essential step in translating the user’s intentions into control commands for the MP.The MP is designed to be controlled by the user’s brain signals, where a communication and control interface board facilitates the transmission of classification outputs obtained from the MI recognition phase. In this way, users can effectively command the MP using their brain activity.To provide an immersive experience, the system incorporates a VR device. The movement of the MP is captured through an IMU with a Bluetooth connection, allowing real-time data transfer to a specific computer running the VR environment, created by us. This VR device offers visual feedback to the user, creating an interactive and engaging virtual environment during the rehabilitation process, in order to ensure the user’s motivation. In the virtual environment, there is a first-person avatar seated on a tricycle and a real customized MP.The system employs a script developed in Python3 (Version 3.8.0, Python Software Foundation, Wilmington, DE, USA) and an MQTT protocol for efficient communication between the various components. Notably, OpenViBE (Version 3.5.0, Free Software Foundation, Boston, MA, USA), Python3, and the MQTT broker operate simultaneously on a single laptop, streamlining the system’s performance.The MI-BCI proposed in this study is divided into two phases: the calibration phase and the online phase. During the calibration phase, EEG signals collected during rest, motor imagery, and passive pedaling are processed to create a robust classification model for MI recognition. Subsequently, in the online phase, this model is employed, enabling the user to command the customized motorized pedal accurately.In summary, the developed system composed of MI-BCI and VR with a pedal end-effector offers a promising approach to neurological rehabilitation. By leveraging brain signals to control the MP and providing real-time visual feedback through VR, the system aims to enhance motor function and facilitate effective rehabilitation processes. The integration of these components on a single laptop ensures efficiency and practicality in its implementation.The motor training was conducted immediately after the tDCS application. The pedaling exercise lasted 20 min, and the training sessions were carried out at the same time of the day, five times a week, for 3 weeks.