# Human-Computer Interaction Multi-Task Modeling Based on Implicit Intent EEG Decoding
## 2 Related Research
### Research on Algorithm Model Construction  
In terms of implicit intent feature extraction, the existing research mainly uses EEG phase synchronization features, frequency domain features, and EEG spatial characteristics:  
The first type of feature extraction method is based on the phase synchronization perspective of multi-channel EEG signals and extracts the phase-locked value (phase locking value, PLV) of several electrode pairs of synchronous responses to participate in intention modeling. [PERSON] et al. (2015) used the EEG multichannel signal phase synchronization method, and the PLV values with the maximum PLV value in theta, alpha, beta-1, and beta-2 under informational/navigational intention were input into three classifiers: SVM, GMM, and NB. SVM has the highest classification accuracy with an average accuracy of 63.6%; the frequency band with the largest PLV difference is theta and alpha, and the PLVvalue of visual search intention with target was significantly larger than the PLV value without target [24]. [PERSON] et al. (2014) also studied implicit intent from the multi-channel signal phase synchronization perspective. They compared the size and changes of EEG PLV values under two visual browsing intentions and found that the classification accuracy of integrating EEG and eye movement signals was about 5% higher than that based on single EEG data modeling [29]. The other feature extraction method uses the traditional EEG frequency domain analysis method to extract energy-related features in different frequency bands. [PERSON] et al. (2021) used the fast Fourier transform method to extract the spectrum related to intention, divide the data window according to the shortest decision time, and model the Shannon entropy SMI (Shannon mutual information) in the model as the feature ([PERSON] et al., 2012). This study found that the theta bands in the frontal, parietal, and occipital regions were most associated with preference decisions, including alpha in the frontal and temporal regions, and they also found clear and significant changes in brain waves in the frontal, temporal, and occipital regions when consumers make preference decisions. What also uses EEG frequency domain analysis is [PERSON] et al. (2019). Their team, combined with the EEG time domain and frequency domain PSD characteristics, explored the driver's emergency braking, flexible braking, and normal driving intentions and the differences between the three intentions. They found that braking intention can cause a 40-60 Hz PSD enhancement significantly, and PSD emergency braking increased more, with a shorter duration. Using the SVM classifier for brake intention recognition, identification accuracy was more than 74% [32]. [PERSON] (2017) studied the physiological changes when users make the click decision from the perspective of multimodal physiological signal fusion and modeled the click decision based on the physiological signals. They selected pupil diameter-related data as an eye movement feature and 789 EEG characteristic values such as frequency domain, time domain, frequency domain, and complexity feature. At last, select 15 characteristic value input classifiers using the random lasso method. This study found that the neural network and logit modeling effects are good; the classification accuracy of logit reached 71.09% [25]. The third type of method uses the spatial mode characteristics of EEG to find the binary classification hybrid intention spatial filter through the CSP algorithm and modify the original signal elements of the intention EEG through the spatial filter to obtain the spatial filter characteristics. The CSP algorithm was originally a two-classification feature extraction algorithm, which can be improved for multi-classification feature extraction. Based on D-S evidence theory, [PERSON] et al. (2021) fused EEG CSP features and eye movement data for multi-task HCI intention identification.