In recent years, various studies have demonstrated the potential of electroencephalographic (EEG) signals for the development of brain-computer interfaces (BCIs) in the rehabilitation of human limbs. This article is a systematic review of the state of the art and opportunities in the development of BCIs for the rehabilitation of upper and lower limbs of the human body. The systematic review was conducted in databases considering using EEG signals, interface proposals to rehabilitate upper/lower limbs using motor intention or movement assistance and utilizing virtual environments in feedback. Studies that did not specify which processing system was used were excluded. Analyses of the design processing or reviews were excluded as well. It was identified that 11 corresponded to applications to rehabilitate upper limbs, six to lower limbs, and one to both. Likewise, six combined visual/auditory feedback, two haptic/visual, and two visual/auditory/haptic. In addition, four had fully immersive virtual reality (VR), three semi-immersive VR, and 11 non-immersive VR. In summary, the studies have demonstrated that using EEG signals, and user feedback offer benefits including cost, effectiveness, better training, user motivation and there is a need to continue developing interfaces that are accessible to users, and that integrate feedback techniques.

The high potential for creating brain-computer interfaces (BCIs) and video games for upper limb rehabilitation has been demonstrated in recent years. In this work, we describe the implementation of a prototype BCI with feedback based on a virtual environment to control the lateral movement of a character by predicting the subject’s motor intention. The electroencephalographic signals were processed employing a Finite Impulse Response (FIR) filter, Common Spatial Patterns (CSP), and Linear Discriminant Analysis (LDA). Also, a video game was used as a virtual environment, which was written in C# on the Unity3D platform. The test results showed that the prototype implemented based on electroencephalographic signal acquisition has the potential to take on real-time applications such as avatar control or assistive devices, obtaining a maximum control time of 65 s. In addition, it was noticed that the feedback in an interface plays a crucial role, since it helps the person not only to feel motivated, but also to learn how to have a more consistent motor intention and when little calibration data is recorded, the probability that the system makes erroneous predictions increases. These results demonstrate the usefulness of the development as support for people who require some treatment in the form of upper limb motor rehabilitation, and that the use of virtual environments, such as video games, can motivate such people during the rehabilitation processes.