# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Reactive BCI
#### 4.1.2 Human-Robot Interaction and Control  
[PERSON] et al. [17] also tested the combination of BCI-AR technology with four user studies and tested their results by controlling a mobile robot through the HoloLens device. To record the EEG data, a g.USB amplifier with six electrodes was used. The multi-class common spatial pattern (CSP) was used to filter the data, and linear discriminant analysis (LDA) was used to classify the signal into one of the three classes. The robot was controlled by the three directional commands: forward, rightward, and leftward. [PERSON] et al. made several attempts [18; 23; 31] to integrate BCI with AR. At first, they explored the viability of combining Epson Moverio BT-200 smart glasses with BCI to enhance human-robot interaction in the Industry 4.0 framework. Single-channel Olimex EEG-SMT was employed to acquire the EEG signals, and the fast Fourier transform (FFT) of the EEG signal was calculated and visualized for the frequency range of interest. One participant was tested on the two-class AR-BCI system featuring a simultaneous display of two flickering icons. In a later attempt, they proposed a wearable monitoring system for inspection in the framework of Industry 4.0. They combined Olimex EEG-SMT, using one electrode, with Epson Moverio BT-200 AR HMD (Epson, Suwa, Nagano, Japan). To process the EEG signal, a simple power spectral density (PSD) analysis was first conducted, followed by a digital bandpass finite impulse response (FIR) filter and a fast Fourier transform (FFT) for feature extraction. To evaluate the system, 20 participants tested the prototype and tried to control the system using two commands. The results indicated that the accuracy was better when the acquisition time for the SSVEP signals was higher. In their most recent attempt, they proposed the adoption of machine learning (ML) classifiers in order to improve the performance of highly wearable, single-channel BCIs. The EEG data were collected using the Olimex EEG-SMT (Olimex Ltd., Plovdiv, Bulgaria), featuring one active electrode, while the Epson Moverio BT-200 smart glasses were utilized to display the two flickering targets. To process the EEG signals, an FFT was performed in the frequencydomain, followed by the application of a bandpass filter within the time domain, restricting the signal to frequencies between 5 and 25 Hz. For the classification process, the selected ML classifiers were support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural network (ANN). The experiment involved the participation of 20 volunteers, and two evaluation metrics were utilized to assess performance: classification accuracy and acquisition time. [PERSON] et al. [22] designed a four-command SSVEP-BCI system combined with HoloLens to control a robotic arm. Nine channels from the Neuracle EEG amplifier were used in this study. EEG signals were downsampled to 250 Hz, and a notch filter at 50 Hz was applied. The FBCCA algorithm was used to classify the data. Twelve subjects participated in the online experiment, and the evaluation metrics employed for this study were the mean classification accuracy, ITR, and time to complete a freely controlled robot movement, with respective results of 93.96%, 14.21 bits/min, and 107.67 s.  
[PERSON] et al. [24] aimed to design and evaluate a high-speed online eight-class SSVEP-based BCI in an OST-AR environment and test it by controlling a robotic arm. The proposed hardware consisted of an eight-channel EEG device designed in their laboratory and a HoloLens HMD. The EEG signals were bandpass filtered from 7-90 Hz and notch filtered at 50 Hz. To classify their data, they used extended CCA and ensemble TRCA. A total of 10 subjects took part in the online robot arm control task, resulting in an ITR of 45.57 bits/min. [PERSON] et al. [30] designed a four-target AR-based BCI-SSVEP for human-robot interaction.