## 4 Results and Discussion
### Comparison with Other Works
In the EOG mode, the HMI can generate four different output commands by using three-channel EOG signals to recognize four kinds of eye movements, including eye blink, wink, gaze, and frown. In the EEG mode, three kinds of ERP components in EEG are extracted as features, including P300, N170, and VPP. Although the HMI of [36] did not use SSVEP to build any communication/control channels, their hybrid HMI system successfully utilizes physiological signal (_i.e._, EOG by eye movements) other than brain activity (_i.e._, EEG) to create control channels. The eye-movement detection could also be introduced to our system to provide more communication channels in the future. P300 detection plays a critical role in our work and the studies of [20; 35; 36]. In [35], LDA and SVM are adopted as the P300 detectors, and their results show that SVM performs better than LDA. SVM is adopted as the detector in [20], while the detector in [36] is LDA. Our results showed that the proposed P300 detector Kernel FDA + SVM outperforms SVM in all cases in terms of classification accuracy. Additionally, the improvement in classification accuracy is particularly large when the number of intensification rounds is small. The reported results have suggested that Kernel FDA + SVM can serve as a better P300 detector than the widely used LDA and SVM. The work of [42] develops an independent component analysis (ICA)-based spatial denoising algorithm (SDA) as a preprocessing to enhance the SNR of P300. The SDA method decomposes 14-channel EEG signals into independent components (ICs), and then automatically identifies \(m\) P300 ICs (_i.e._, the ICs accounting for the P300) from the decomposed ICs using an index called integrated distribution strength. The selected P300 ICs are used to reconstruct waves. Other commonly used features including time-domain, frequency-domain, and wavelet features are extracted from the denoised waves. A best feature subset is further determined based on F-score measure, and fed into an SVM classifier for P300 detection. Their results show remarkable P300 classification accuracy of over 95% for single-trial EEG. However, their method requires a certain number of electrodes to run ICA in order to guarantee the efficiency of the SDA. The adaptive nonlinear principal component analysis (ANPCA) [43] and ICA-based method [44] are also blind source separation-based spatial filtering methods to enhance the SNR of P300, and therefore a large number of EEG channels is required (12 for the former and eight for the latter). However, these promising spatial filtering techniques could not be directly applied to our current system because only two electrodes at Pz and Cz are used for P300 detection in our current BCI system. Nevertheless, it can be introduced as a preprocessing step to enhance the SNR of P300 when the number of electrodes in our BCI system increases in the future. Moreover, our experimental results have demonstrated the advantage of the proposed KernelFDA+SVM P300 detector over the conventional SVM in terms of P300 classification. The proposed detector is a generic model and might be applied to other P300 BCIs to further improve their systems' P300 detection accuracies. [PERSON] _et al._[45] propose an automatic channel selection algorithm for P300 ERP classification. A multiobjective hybrid real-binary particle swarm optimization (MHPSO) algorithm-based channel selection method, and a Fisher discriminant analysis (FDA) classifier is used for P300 classification. Given a huge number of channels, the MHPSO algorithm is used to optimize the FDA parameters and find a set of fewest EEG channels by maximizing the classification accuracy and minimizing the number of channels simultaneously. Their experiment and analysis are carried out on datasets of auditory ERPs from sound in which the EEG raw data are recoded using 64 EEG channels. Their experimental results show that it is possible to greatly reduce the number of channels necessary for classification without sacrificing the classification accuracy.