# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
## 4 Simulation Results
### Discussion  
When classifying the data obtained from healthy subjects, the LDA-\(k\)NN voter achieved better results and outperformed the SVM-based voters by about 0.33%. This difference may not seem significant; however, considering the low computational complexity of the LDA-\(k\)NN fusion, this classifier is better to use when training on larger datasets. However, when using smaller datasets, it is preferred to add SVM into the ensemble model, as it will provide more accurate results. The weighted ensemble voting model with the SVM classifier provided the best performance for ALS patients' data, achieving more than 90% accuracy when using four-channel classification. There was a tradeoff between the accuracy and the computational complexity. For large datasets, the LDA-\(k\)NN voter will be the better option. However, W-LDA-SVM-\(k\)NN provided more accurate results, but as it requires much more time for training, it is better to use on smaller datasets.  
Apart from the tradeoff between the computational complexity and the accuracy, there was one general weakness, related to memory requirements. Due to the fact that \(k\)NN is an instance-based algorithm, it must store the training data. This may cause problems when using more training data for online spellers. Nevertheless, 1000 data samples, which were vectors containing 204 data points, were enough for an efficient result; thus, data storage should not cause significant limitations.  
By comparing different numbers of channels, it turned out that using only four channels of the parietal zone was more efficient than using a wider range of brain activity with eight channels. The summary of the results for ALS patients' data with different number of EEG channels is presented in Table 6. The accuracy improved by about 5% (on average) when using the four channel EEG data. Single-channel EEG classification provided less than 80% accuracy, which was another limitation found during the experiments. However, if the single-channel EEG timeseries are converted to a frequency domain, the accuracy may increase as in [47]. Thus, to decrease the number of used electrodes in the future, it is planned to use frequency domain spectrograms instead of EEG timeseries.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Recall (\%)** & **F-Score (\%)** \\ \hline \multicolumn{4}{c}{**Testing on healthy subjects**} \\ \hline Gradient Boosting & 90.17 & 89.32 & 89.97 \\ XGBoost & 91.68 & 91.51 & 92.80 \\ \hline LDA & 89.15 & 88.99 & 89.06 \\ kNN & 86.58 & 86.42 & 87.10 \\ SVM & 87.51 & 87.21 & 87.49 \\ \hline LDA-kNN & 91.51 & 91.12 & 91.40 \\ LDA-SVM-kNN & 91.10 & 90.90 & 91.00 \\ W-LDA-SVM-\(k\)NN & 91.24 & 90.91 & 91.17 \\ \hline \multicolumn{4}{c}{**Testing on ALS patients**} \\ \hline Gradient Boosting & 78.02 & 77.01 & 77.46 \\ XGBoost & 79.95 & 78.93 & 79.59 \\ \hline LDA & 73.15 & 72.89 & 73.11 \\ kNN & 70.58 & 70.53 & 70.05 \\ SVM & 76.98 & 77.82 & 77.09 \\ \hline LDA-kNN & 77.86 & 76.93 & 77.37 \\ LDA-SVM-kNN & 77.98 & 77.36 & 77.42 \\ W-LDA-SVM-kNN & 78.74 & 78.15 & 78.62 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test results using Pz channel data.  
The proposed methodology allows training a universal P300 speller, which does not need to be retrained for each subject. Despite the fact that the classification was performed offline, it is assumed that the same tendency will be noted for the online P300 speller as well. Therefore, ALS patients will not face the necessity of sitting for an hour in front of the flashing GUI for the speller to collect the training set. The proposed features classification methodology makes the P300 speller ready for exploitation right from the first trial.