Parkinson’s disease affects a patient’s mobility because signals from the brain no longer reach the muscles. But the intention behind movement often remains intact - measurable and unique. This project presents a personalised neurotechnology for Parkinson’s patients. A multimodal transformer model was trained on EEG, EMG, and IMU data collected from myself and, in the future, from my father (who has Parkinson’s disease) to classify movement intentions in real time. I built the EEG hardware and designed the system based on an extensive literature review. A 6-axis robotic arm serves as proof-of-concept, controlled in real time by my biosignals. Future versions may restore motion via focused ultrasound and electrical muscle stimulation - creating a direct link from thought to movement.