# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Reactive BCI
#### 4.1.3 IoT Applications
These metrics assessed and measured the neural responses and time delays associated with the experimental task. [PERSON] et al. [32; 35] addressed the adoption of ML classifiers and CNN to improve the performance of highly wearable single-channel BCIs. The suggested system relied on classifying SSVEPs. They combined a single-channel EEG acquisition device with four different AR HMDs. The signal processing stage involved an FFT in the frequency domain and the appliance of a bandpass filter between 5-25 Hz in the time domain. Finally, the classification process involved three ML classifiers, specifically, SVM, KNN, and ANN. In the initial experiment, 20 subjects participated, while in the subsequent three experiments, there were nine subjects each. Furthermore, the first HMD featured two flickering targets, while the other three HMDs had four targets each. The evaluation metric utilized for this study was classification accuracy. In another attempt to improve the performance of highly wearable reactive BCIs, they proposed the adoption of an innovative algorithm (ANN) with a learnable activation function. In the experimental campaign, 20 volunteers participated, and each volunteer underwent single-channel EEG acquisition. Epson Moverio BT-200 smart glasses were used to display two flickering icons during the experiment. Classification accuracy was the evaluation metric employed for this study, and the results indicated that the ML classifier can outperform other processing strategies, such as CCA. [PERSON] et al. [33] proposed an AR-based BCI-SSVEP system with three to four commands for wheelchair navigation. The system was composed of Epson Moverio BT-35E smart glasses and a four-channel g.MOBllab+. The EEG signals were first subjected to a 0.5-100 Hz bandpass filter, and then the relevant features were extracted using CCA. For the classification stage, LDA, KNN, and SVM were employed. In the online experiment, 12 subjects participated, and the evaluation metrics used for assessing the system's performance were classification accuracy and ITR. In a similar attempt, [PERSON] et al. [45] developed a BCI system to control an electric wheelchair using audiovisual stimuli from MR goggles and virtual sound sources. The system components included an electric wheelchair (YAMAHA JWX-1), MR goggles (HoloLens2), wired earphones (ALPEX HR-3500 BK), and four EEG electrodes (Polymate Mini AP108). The classifiers for visual and auditory stimuli were evaluated using leave-one-out cross-validation, achieving average classification accuracies above 70% and 50%, respectively. Online analysis showed target selection accuracies of 37.1% for visual markers and 25.7% for sound, with visual selection significantly higher than chance level. The lower online accuracy was attributed to marker flashing certainty and sound distinction difficulties for some participants. [PERSON] et al. [34] proposed a protocol for SSVEP-based neurofeedback training to alter attention with emotional biases using a portable AR-BCI. The five participants were instructed to focus their attention on the task-relevant stimulus, which was a semi-transparent Gabor patch, and to disregard the emotional distractor, represented by an angry or sad face. Each stimulus was flickering at a specific frequency (8.57 or 12 Hz). Signal acquisition was performed with an EEG cap containing 16 electrodes. FFT was used to detect the distinct response of visual stimuli tagged with specific frequencies. [PERSON] et al. [36] designed an AR-BCI system that detected objects in a 3D space using depth sensors and ML. The EEG signals were acquired using the Polymate Mini AP108, which used two electrodes. On the other hand, for displaying the four stimuli, HoloLens 2 was utilized. Classification accuracy was the evaluation metric employed for this study. [PERSON] et al. [37] combined computer vision (CV) and AR with a brain-controlled wheelchair. They used five active electrodes to acquire the EEG signals and Epson BT-350 to display the stimuli.