# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
## 4 Simulation Results
### Four-Channel Data Simulations  
Multichannel EEG classification allows covering different regions of the human brain; however, it makes it much more complex. It has been shown by the comparison of 14-channel data and 4-channel data classification that increasing the number of EEG electrodes does not increase the accuracy of the P300 speller [44]. For instance, decreasing the number of channels from 64 to 20 using channel selection and nontarget data reduction provided better results in terms of computational complexity and did not affect the accuracy negatively [45]. The EEG channels can be efficiently selected using different methods, such as abchannel-aware dictionary with sparse representation for the P300 speller as in [45]. Group sparse Bayesian linear discriminant analysis (BLDA) can also be applied for channel selection. As reported in [46], by applying group sparse BLDA to the data collected from 16 different subjects, it was found that the most optimal channels selected by the algorithm were located close to visual ERP areas. Despite the fact that optimal EEG channel selection is subject-dependent, the abovementioned results lead to the common idea that the selected channels should be located in the parietal and occipital zones of human brain. The presented results show that the CPz, P4, P3, Pz, O1, Oz, and O2 channels were the most efficient electrodes for the majority of the subjects using most of the channel selection methods. Authors recommend using the Pz, Oz, O1, and O2 combination of channels as the most efficient [46]. In order to check whether the number of EEG channels can be decreased without affecting the accuracy negatively, experiments have been conducted using four EEG channels. The four channels used for the experiment were chosen from the visual ERP area of a human brain, according to the results that were presented by other researchers. The channels selected were P3, P4, Pz, and Oz. The placement of the electrodes is represented in Figure 4b. The combination of PO7, PO8, Pz, and Oz was also tried during the experiments; however, its accuracy was on average about 3.5% lower than the combination of P3, P4, Pz, and Oz. Table 4 presents the obtained results for four-channel data features classification. Testing on ALS patients using four channels generally improved the classification performance among the proposed ensemble models. Comparing these results with the eight-channel experiments (see Table 3), the F-score increased on average by more than 5% for the proposed ensemble models, which did not use CNN. In contrast, the performance of the LDA-SVM-\(k\)NN-CNN model decreased by more than 10%. This is explained by the structure of the CNN classifier, which is very dependent on the input shape. That is another reason why the CNN is inefficient for EEG time-series classification in the P300 speller. Every time the number of channels is changed, the architecture of the CNN classifier should be changed too, which is a very complex procedure. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Recall (\%)** & **F-Score (\%)** \\ \hline Gradient Boosting & 85.01 & 83.56 & 85.98 \\ XGBoost & 89.95 & 87.42 & 88.21 \\ \hline LDA & 78.09 & 77.48 & 77.02 \\ kNN & 82.52 & 82.21 & 82.15 \\ SVM & 83.98 & 84.01 & 83.79 \\ CNN & 68.26 & 65.13 & 64.49 \\ \hline LDA-\(k\)NN & 84.79 & 81.00 & 82.95 \\ LDA-SVM-\(k\)NN & 84.20 & 84.97 & 83.99 \\ LDA-SVM-\(k\)NN-CNN & 76.04 & 73.89 & 74.45 \\ W-LDA-SVM-\(k\)NN & 85.36 & 83.97 & 86.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test results using 8-channel data of ALS patients. It is observed from Table 3 that the weighted ensemble voting classifier achieved a 90.74% F-score, which was higher than the gradient boosting with 88.59%. The proposed simple averaging voters achieved 89.17% using LDA-\(k\)NN architecture and 88.88% using LDA-SVM-\(k\)NN fusion, which was also better than the gradient boosting algorithm.