Rehabilitation plays a fundamental role in promoting functional independence for individuals with motor impairments. Virtual rehabilitation systems have demonstrated benefits for engagement and motor performance; however, most existing systems adjust task difficulty solely on the basis of motor performance metrics, while neglecting emotional states that influence motivation and adherence. This study proposes an affective computing-based virtual rehabilitation approach that integrates electroencephalography-derived emotional metrics into real-time difficulty adjustment. The system combines hand tracking with a Leap Motion Controller and affective monitoring via the EMOTIV Insight brain-computer interface. Stress and interest indicators are continuously analyzed to dynamically adjust task difficulty by modulating an in-game agent's speed. A preliminary user experience evaluation using the User Experience Questionnaire was conducted with 8 participants, divided into control and experimental groups. The experimental group exhibited higher scores in perspicuity (1.75 vs 0.56) and stimulation (1.37 vs 0.87), suggesting that integrating affectively driven adaptation enhanced perceived clarity and motivational engagement. These preliminary results suggest that the emotion-aware version improved perceived perspicuity and stimulation compared to the task-oriented version, while maintaining stable efficiency and dependability. These findings suggest that incorporating affective metrics as primary adaptation variables enhances engagement and supports a more balanced challenge-skill balance in virtual rehabilitation environments.