# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Reactive BCI
#### 4.1.3 IoT Applications
A highpass filter was employed to eliminate frequencies above 0.5 Hz, while a lowpass filter was used to remove frequencies below 50 Hz. Additionally, another lowpass filter was applied to eliminate frequencies below 12 Hz. The linear support vector machine (SVM) classifier was utilized in terms of classification. To evaluate their system, six subjects took part in the experiment, and the performance metric was employed, which can be defined as the ratio of the number of trials for a correctly predicted target to the total number of trials for each posture. The results showed that there were no significant differences in BCI performance in regard to posture. [PERSON] et al. [27] present a robot grasping experiment that was designed to verify the applicability of the AR-BCI. The Neuracle EEG Recorder, equipped with nine electrodes, was integrated with the HoloLens HMD to create the BCI-AR system. The FBCCA algorithm was used to classify the flickering stimuli. Twelve subjects participated in the online experiment, and they were able to successfully control the robot. The evaluation metrics employed for this study were classification accuracy and ITR. In a different study, [PERSON] et al. [41] created SSVEP flickering stimulation interfaces that featured four different numbers of stimulus targets to examine the impact of stimulus numbers on SSVEP-BCI within an AR context. SynAmps2 with 64 electrodes was used as the acquisition device, and a bandpass filter between 5 and 90 Hz was applied. The researchers employed CCA, FBCCA, and TRCA for the classification process. The results indicated that the recognition accuracy decreased as the number of stimuli increased in the AR-SSVEP setup. Also, in a later work, [PERSON] et al. [44] developed a BCI system based on SSVEP to enhance the practical application and interaction capabilities of prosthetic hands for disabled patients. The study introduced an asynchronous visual stimulus paradigm using AR with eight control modes (grasp, put down, pinch, point, fist, palm push, hold pen, and initial) and proposed a new pattern recognition algorithm, Center-ECCA-SVM, combining center-extended canonical correlation analysis and support vector machine. Additionally, an intelligent BCI system switch based on the YOLOv4 deep learning object detection algorithm was proposed to enhance user interaction. The results showed that the AR paradigm significantly improved the average SSVEP spectrum amplitude and SNR compared to the liquid crystal display (LCD) paradigm. The proposed Center-ECCA-SVM classifier achieved high asynchronous pattern recognition accuracy, and the YOLOv4-tiny model demonstrated effective real-time detection of the prosthetic hand. The system's practicality was validated through real-life task completion, showcasing its effectiveness and user acceptability. [PERSON] et al. [28] designed a biometric authentication system based on EEG by utilizing the rapid serial visual presentation (RSVP) paradigm with stimuli of photographs of people displayed on AR HMD. During the experimental trial, 10 photographs depicting faces were presented to the subjects in a randomized sequence. Within this set, one photograph portrayed a person familiar to the subject (referred to as the \"target\"), while the remaining nine photographs displayed faces of individuals unknown to the subject (referred to as \"non-targets\"). To obtain the EEG data, a 64-channel BioSemi ActiveTwo system was employed. The preprocessing stage consisted of downsampling the signal from 2048 Hz to 512 Hz and applying a bandpass filter from 0.1-50 Hz. To perform the classification stage, the researchers employed four distinct machine learning classifiers: linear SVM (LSVM), k-nearest neighbor (KNN), LDA, and decision tree (DT) models. A total of 20 participants actively participated in the experiment, and the results showcased exceptional performance and accuracy. The evaluation of this experiment involved two key metrics: the amplitude of event-related potentials (ERP) and the latency.