# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 1 Introduction
Consequently, the medical services opt to use emerging assistive technologies such as remote rehabilitation (RR) or telerehabilitation [4] because they can be implemented using either portable or wearable systems, such as EEG-based BCIs, or robotic systems. RR is becoming an alternative to bring rehabilitation to larger human populations and maintain the same benefits in remote communities where the lack of health facilities and infrastructure is evident. Implementing RR technologies has numerous advantages [5], such as providing the possibility of developing a rehabilitation strategy in places far away from a medical rehabilitation center and the opportunity to have a rehabilitation environment similar to the one in which daily activities take place, rather than in a rehabilitation-controlled facility. Figure 1: Rehabilitation process. RR techniques also allow for monitoring health conditions and registering faster rehabilitation advances, among others. Additionally, this has some cons, such as the cost of the final system used for virtual rehabilitation, which could be expensive, or the impossibility of using an electrical supply and/or Internet in remote places such as mountains or rural environments. In view of the increasing interest in RR, this document presents a review of the literature that contains various studies on the application of BCI and emerging technologies, such as virtual reality (VRy), for RR and its relative disciplines. The present research aims to identify relevant works and BCI integration techniques that have achieved the best practical application performance. This performance is measured using the information in different databases, the signal feature-extraction techniques of EEG, and the algorithms for the selection and classification of this type of signal. Previous studies [6] lead us to find the best models for this task and could bring us the opportunity to improve different tasks, and in the future, apply these models in other areas. This document aims to comprehensively analyze the current results on BCI for virtual rehabilitation and remote technology on EEG for assistive devices. We propose this review, given the relevance of virtual rehabilitation for assistive devices and taking into account the lack of bibliography sources that integrate these technologies. This review document is organized as follows: Section 1 presents a brief introduction to the concepts mentioned in the present work. Section 2 presents the research performed in Google Scholar, Web of Science, and Scopus. Section 3 provides a statement of the technology that allows a BCI and how these devices are used for rehabilitation. Section 4 presents the EEG databases most employed for BCI systems. Sections 5 and 6 introduce the concepts of feature extraction and selection and the different techniques for obtaining them, respectively. Section 7 mentions the information about the machine learning (ML) techniques for EEG classification. In Section 8 are described the works related to BCI technology, and Section 9 explains how these BCIs are implemented in remote and virtual rehabilitation. Finally, Section 10 discusses how these emerging technologies could be used to improve the performances of BCIs and the consequent rehabilitation using virtual technologies.