# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 8 Brain-Computer Interfaces
The increasing number of studies in recent years has created the necessity to review some of them, so there exist different reviews that are focused on the BCIs, their trigger signals, their methods of composition, and other important concepts. For example, [PERSON] and coauthors [14] present the techniques and challenges in BCI, explaining the different techniques for feature extraction, selection, and classification. Additionally, the work in [76] is more focused on the classification algorithms for EEG signals, such as adaptive classifiers, matrix, and tensor classifiers, transfer learning methods, and deep learning methods. On the other hand, the study of [77] focuses on the different BCI paradigms (P300, Evoked Potential, etc.) and how they are implemented. This work includes classification methods in the analysis of the paradigms. The review presented in [47] is about the deep learning algorithms that try to classify the motor/imagery condition of the signals of EEG and also presents trends and information about the feature extraction, signal classification, and normalization process. This review only contains papers that use EEG signals as a base. [PERSON] et al. [54], through a systematic review, analyzes the deep learning techniques and the architectures of the algorithms for EEG classification, and the EEG frequencies and the tasks that the studies reflect. On the other hand, [78] shows information about the countries that develop BCI technologies, the paradigms of EGG (that in this study are called control signals), the concept of BCI being invasive or non-invasive, and the software that is useful for BCI implementation. Finally, the most recent review of EEG BCI is focused on the application of BCIs for the rehabilitation of a patient after a stroke; it specifies the clinical outcomes and how the studies were designed [79]. In [14], several suggested branches of BCI applications are proposed. One is application-oriented toward monitoring a mental state, and the other is oriented toward controlling or manipulating an external device. Additionally, these BCI can be found in diverse fields, such as entertainment, medical, art, and gaming (see Figure 10[14]). Today many of these applications are non-invasive EEG applications, such as the ones described in [16], so the industry created EEG commercialized devices to register the signals. From the control point of view, these devices are categorized through their resolution: the number of channels or signals provided to find low, medium, or high-resolution devices. This resolution also determines the signal's sample rate, the number of channels, and other characteristics. The non-invasive products are mostly \"wearable\" to enhance their wireless, portable, and affordable features [66]. These kinds of devices are used for the previously mentioned purposes. For example, the review work in [80] measures the commercial devices (such as Neuroskan and Emotive) of EEG and the fields where they were applied. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Author(s)** & **Year** & **Biosginal** & \begin{tabular}{l} **Classification** \\ **Algorithms** \\ \end{tabular} & **Application** \\ \hline [PERSON] et al. [72] & 2017 & EEG\&EMG & \begin{tabular}{l} **Treshold SSEVP** \\ **end** & \begin{tabular}{l} **Control of a upper-limb** \\ **transhumeral prosthetic** \\ **arm open/close** \\ \end{tabular} \\ [PERSON] \& [PERSON] [73] & 2018 & EEG\&EMG & \
\begin{tabular}{l} **QDA, KNN and** \\ **Quadratic SVM** \\ **Neural Network,** \\ **SVM** \\ \end{tabular} &
\begin{tabular}{l} **Control of upper limb** \\ **Control of wearable** \\ **assistive device.** \\ **Identification of** \\ **Motor/Imagery** \\ **of Left and Right hand** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: BCI studies.