# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
## 5 Conclusions  
In this paper, four different ensemble voting models based on LDA, SVM, \(k\)NN, and CNN classifiers were proposed. The experimental results suggest that the proposed ensemble voting classifiers trained on the data from healthy subjects are able to classify bulbar and spinal onset ALS patients' data. The proposed ensemble voting based on LDA, SVM, and \(k\)NN classifiers provided robust results when tested on different subjects. The W-LDA-SVM-\(k\)NN weighted ensemble voting model achieved the most accurate results among the proposed classifiers, reaching 91.34% accuracy for four-channel ALS patients' data. When comparing the proposed methods by the time elapsed during the training, the most efficient classifier turned out to be the LDA-kNN combination, which achieved a good accuracy of 99.92% for eight-channel data of healthy subjects but provided less than 90% accuracy for ALS patients. Almost all of the proposed ensemble voting models (except for the CNN-based model) outperformed standalone classifiers by about 5% during the experiments on eight-channel, four-channel, and single-channel data. The ensemble model with CNN turned out to be inefficient for timeseries classification in a subject-independent P300 speller. It is planned to extend the methodology in the future and test the given subject-independent models using data from patients suffering other motor neuron diseases, such as cerebral palsy or peripheral neuropathy. Moreover, while using the online P300 speller, the users can be tired and their mental workload may affect the classification as well. Thus, mental workload classification of EEG [48] is also planned to be used in future research. The possible usage of a spectrogram representation of the EEG signal is also considered being combined with ensemble learning in the future. EEG data can be presented as an intertrial coherence plot or event-related spectral power (ERSP) spectrograms [49] and processed simply as images. In that case, transfer learning may be used in the future together with more advanced CNN architectures such as ResNet [50]. Conceptualization, A.M.; methodology, A.M.; software, A.M.; validation, A.M., P.K.J. and M.T.A. ; formal analysis, A.M. and P.K.J. ; investigation, A.M.; resources, P.K.J. and M.T.A. ; data curation, A.M., P.K.J. and M.T.A. ; writing--original draft preparation, A.M.; writing--review and editing, P.K.J. and M.T.A. ; visualization, A.M.; supervision, P.K.J. and M.T.A. ; project administration, P.K.J. and M.T.A. ; funding acquisition, P.K.J. and M.T.A. All authors have read and agreed to the published version of the manuscript. The work presented in this paper was carried out by the first author as a part of her MSc Thesis (in Electrical and Computer Engineering) at Nazarbayev University. This research was supported by the Faculty Development Competitive Research Grants Program under the grant numbers 110119 FD4525, 021220 FD0251, and the Collaborative Research Grants Program under the grant number 091010 CRP2116. Not applicable. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Type of Data** & **LDA-kNN** & **LDA-SVM-kNN** & **LDA-SVM-kNN-CNN** & **W-LDA-SVM-kNN** \\ \hline
8-channel data & 84.79\% & 84.20\% & 76.04\% & 85.36\% \\
4-channel data & 89.33\% & 90.02\% & 65.00\% & 91.34\% \\ single-channel data & 77.86\% & 77.98\% & - & 78.74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Test results accuracy using ALS patients’ data with different channelsData Availability Statement: The datasets are publicly available and the links can be found in the references [31; 32]. Conflicts of Interest: The authors declare no conflicts of interest. The supporting source code is available upon request from the corresponding author. The following abbreviations are used in this manuscript: