# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Reactive BCI
#### 4.1.3 IoT Applications  
[PERSON] et al. [16] investigated the feasibility of an AR-BMI system using grid-shaped (3 \(\times\) 3) SSVEP flickering stimuli displayed on HoloLens HMD. A 32-electrode antiCAP was used to record the EEG signals, which were then classified into one of six classes using shrinkage-regularized linear discrimination analysis (shrinkage-rLDA) with 10-fold cross-validation. One subject participated in the evaluation portion, and the average classification accuracy, which was the evaluation metric for this experiment, was 30.51%. [PERSON] et al. [19] designed four different display layouts and tested the different results by displaying them in HoloLens HMD and on a PC screen. SynAmps2 amplifiers with 64 electrodes were used to record the EEG data. A bandpass filter between 0.5-45 Hz was applied, and the signals were sampled at 1000 Hz. Power spectrum density (PSD) estimation was used to process the data, and CCA was used for the classification. Ten subjects participated in the experiment, and the evaluation metrics employed were classification accuracy, ITR, and power distribution topography. The results indicated that when the stimulus duration is more than 3 s, the AR-SSVEP achieves similar classification accuracy to PC-SSVEP. Apart from the performance variation attributed to display layouts, [PERSON] et al. [25] investigated the effect of ambient brightness on AR-BCI performance by testing five different light intensities on 18 subjects. The SynAmps2 amplifier, with 64 selected electrodes, and HoloLens HMD were the selected hardware for this study. In order to process and classify their EEG data, they used FFT, CCA, and FBCCA. To enable the SSVEP recognition algorithm to adjust to varying light intensities, they introduced a novel optimization algorithm called ensemble online adaptive CCA (eOACCA). The purpose of this algorithm was to enhance the adaptability of the SSVEP recognition algorithm when faced with changes in light intensity. The results indicated that as the light intensity increases, the response intensity of AR-SSVEP gradually decreases, and there is a corresponding decrease in recognition accuracy as well. Furthermore, the experimental outcomes proved that the proposed eOACCA algorithm outperforms FBCCA and CCA algorithms. In the same context, [PERSON] and [PERSON] [39] explored the impact of different visual stimulus colors on the classification accuracy of SSVEP-BCI. The researchers designed interfaces featuring four distinct colors (white, red, green, and blue) and conducted tests in a combined AR environment and on a conventional PC screen. The NeuSen W acquisition device with 32 electrodes was combined with HoloLens HMD. The classification results were found to be affected by both the visual stimulus colors and the duration of stimulation. [PERSON] et al. [20] designed an AR-BCI system with an eight-class SSVEP stimulus and studied the performance of different algorithms. The hardware used in this study was an eight-channel EEG device developed in their laboratories and the HoloLens HMD. EEG data were filtered by a notch, highpass, and lowpass filter. Extended filter bank canonical correlation analysis (FBCCA) and task-related component analysis (TRCA) were the two algorithms tested. Eight participants took part in the experiment, and the results showed that the extended FBCCA had the best overall performance. [PERSON] and [PERSON] [21] developed a working prototype of BrainChat that featured two-person textual communication. HTC Vive HMD was used in combination with an eight-channel NeuroElectrics Enobio 32. EEG signals were processed and classified with the OpenVibe software. Two subjects tested the prototype system and managed to communicate using their EEG signals. [PERSON] et al. [26] conducted a study to evaluate the performance of BCI in various postures, including sitting, standing, and walking. They utilized a standard EEG cap with 31 electrodes in combination with the HoloLens HMD. For signal preprocessing, the researchers implemented several filters.