# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Passive BCI  
This section is dedicated to passive BCI systems and includes five studies presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Reference** & **Year** & **Subs** & \begin{tabular}{c} **EEG** \\ **Channels** \\ \end{tabular} & **Commands** & \begin{tabular}{c} **Signal** \\ **Preprocessing** \\ \end{tabular} & **Classification** & \begin{tabular}{c} **Feature** \\ **Extraction** \\ \end{tabular} & \begin{tabular}{c} **Evaluation** \\ **Metrics** \\ \end{tabular} \\ \hline [PERSON] et al. [46] & 2015 & 10 & 14 & - & - & - & - & Test Trial Time \\  & & & & Low Pass at & & & \\ [PERSON] et al. [47] & 2019 & 15 & 16 & - & 50 Hz, High & LDA & PSD & Classification \\  & & & & & Pass at 1 Hz, & & & Accuracy \\  & & & & & Notch at 50 Hz & & & \\  & & & & & Band Pass & & & \\ [PERSON] et al. [48] & 2021 & 20 & 16 & - & 3–45 Hz, Notch & CNN & FBCSP & Classification \\  & & & & & filter 50 Hz & & & & Accuracy \\ [PERSON] et al. [49] & 2022 & 10 & 8 & - & - & - & - & - & SUS, NASA-TLX \\ De Massari & 2014 & 5 & 64 & - & Spatial Filter & LDA, SVM & \
\begin{tabular}{c} **S**pectral \\ Estimation \\ \end{tabular} &
\begin{tabular}{c} **Classification** \\ **Accuracy** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Studies from the passive BCI category.  
[PERSON] et al. [46] proposed a novel AR-BCI framework to train the user to regulate his own mental state while performing surgery-like tasks using a robotic system. They combined Emotiv EPOC with Sony HMZ-T1 in order to present the AR-BCI scenario. Ten subjects participated in the experiment, in which they would operate an AR scalpel based on their cognitive value.  
[PERSON] et al. [47] designed an alignment experiment in order to classify user attention as internally or externally directed. They combined a 16-channel g.Nautilus with HoloLens to display the alignment task. To preprocess the EEG data, they used a lowpass filter at 50 Hz, a highpass filter at 1 Hz, and a notch filter at 50 Hz. LDA was utilized in the classification process, and the results of the analysis of 15 participants demonstrated that the classifier reliably predicts the type of attention. In another attempt, [PERSON] et al. [48] explored the feasibility of categorizing a target as real or virtual by analyzing EEG signals using ML techniques. To acquire the EEG data, a g.Nautilus EEG headset with 16 active electrodes was used. The EEG data were bandpass filtered between 3 and 45 Hz and notch filtered at 50 Hz. Results showed that person-dependent classification based on EEG data is possibly better and works more reliably than the classification based on eye-tracking data.  
[PERSON] et al. [49] developed a BCI-AR user interface based on the NextMind and the HoloLens 2. The experiment was structured into two distinct parts. In the first phase, participants were required to visually identify and select a specific component. Once this selection was made, the second phase involved the assembly of these components using a robotic arm. NASA-TLX and SUS questionnaires were used to evaluate the experiment.  
[PERSON] et al. [50] combined BCI in mixed reality (MR) environments in order to decode users' mental states. To acquire the EEG data, a 64-channel actiCAP was utilized, while SVM and LDA classifiers were used for the classification process. The results suggest that LDA had the best accuracy.