# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 8 Brain-Computer Interfaces  
Implementing a BCI requires a control algorithm that could regulate the commands to the computer and could help to improve the interaction between the final user and the system. This is obtained because of the level of interaction between humans and machines decides how versatile and adaptable the robotic system is. For example, the authors of [71] created a BCI to perform a pre-established function. This allows the movement and functioning of various parts of the robot, monitoring to reach the gap between the information in real-time, and the information the BCI needs to work adequately. On the other hand, in the project of [72], a prosthetic arm with six DOF, including an elbow extension, was built. This device allows the opening and closure of the robotic arm; five subjects validated the control scheme. [PERSON] et al. [18] built a BCI with a dataset from six healthy subjects performing tasks such as rest, move, and drink. This information feeds an SVM algorithm and a NN for the classification. The achieved accuracy of classification was 69.4% for the SV and 71.4% for the neural network. Another study was preformed [73] with a BCI hybrid model for EEG and EMG. This study was focused on persons with amputations. The used dataset was a previous one by [PERSON] et al. [74]. This BCI counts with a feature extraction process that includes AR, RMS, WL, WAMP FR, MMNF, MMDF, and entropy; has a feature selection process; and uses three classification algorithms, QDA, KNN, and SVM, achieving accuracies of 98.97%, 98.90%, and 94.82% respectively. In the BCI of [75], the researchers used an Emotiv Epcot device to record C3, C4, and Cz signals and information channels; calculate their DWT; and fed a BiLSTM algorithm that achieved an accuracy of 87.14%. \begin{table}
\begin{tabular}{l l l l l} \hline
**Author** & **Year** & **Dataset** & **Algorithm** & **Accuracy\%** \\ \hline [PERSON] et al. [20] & 2018 & \begin{tabular}{l} Physionet Motor \\ Movement/Imaginary \\ Dataset \\ \end{tabular} & Neuronal & 89.5\% \\ [PERSON] et al. [23] & 2018 & \begin{tabular}{l} Physionet Motor \\ Movement/Imaginary \\ Dataset \\ \end{tabular} & CRNN & 98\% \\ [PERSON] et al. [66] & 2018 & \begin{tabular}{l} Four users of Emotiv. \\ Four Imaginary \\ movements: left hand, \\ right hand, both feet. \\ Left hand brains motor \\ region pre-processing. \\ \end{tabular} & SVM & 85.45\% \\ [PERSON] et al. [67] & 2019 & \begin{tabular}{l} Left hand brains motor \\ region pre-processing. \\ Physionet Motor \\ Movement/Imaginary \\ Dataset \\ \end{tabular} & CNN & Random \\ [PERSON] et al. [68] & 2020 & \begin{tabular}{l} Physionet Motor \\ Movement/Imaginary \\ Dataset \\ \end{tabular} & LSTM & 78.95\% \\ [PERSON] et al. [69] & 2020 & \begin{tabular}{l} Physionet Motor \\ Movement/Imaginary \\ Dataset \\ \end{tabular} & Common & 70\% \\ [PERSON] et al. [69] & 2020 & \begin{tabular}{l} 32 persons. 4 physical \\ activities: left hand, right \\ hand, foot and tongue) \\ \end{tabular} & CNN, & 91.83\% \\ [PERSON] et al. [62] & 2021 & \begin{tabular}{l} BCI Competition IV \\ dataset 2a \\ Physionet(20, 50,80, 109 \\ Subjects) \\ \end{tabular} & SVM-PK & 67.0\% \\ [PERSON] et al. [24] & 2022 & \begin{tabular}{l} Physionet(20, 50,80, 109 \\ Subjects) \\ \end{tabular} & LRT-CNN & 93.86\% \\ [PERSON] et al. [24] & 2022 & \begin{tabular}{l} BCI Competition IV \\ dataset 2a \\ \end{tabular} & CNN-LSTM & 92\% \\ Ghadir \& 2022 & \
\begin{tabular}{l} BCI Competition IV 2a \\ and High Gamma Dataset \\ \end{tabular} &
\begin{tabular}{l} CNN \\ (MBEEG- \\ Net) \\ \end{tabular} & 82.01\% \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._Table 4 presents studies focused on implementing BCI with control devices. For disabled people, the application of machine learning tools could lead to control systems without the necessity of impulsing the movement through a damaged or missing body part. This increases life quality and the chance to reintegrate into everyday activities without external help.