# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
###### Abstract
The intersection of the target intensified row and column would be the character chosen. The objective of this work was to design a robust subject-independent classifier for the P300 speller. In other words, the aim was to design a P300 speller, which could be trained on healthy subjects but provide good results when used by ALS patients, to remove the necessity for ALS patients to train the speller themselves, wasting their time and effort. The classification of brain signals can be performed using different algorithms and methods. Over the last decade, different methods have been applied for ERP-based spellers. P300 identification can be performed using unsupervised, semi-supervised and supervised methods. Unsupervised learning can be applied for calibrating a subject-independent classification model [12]. Subject independent ERP classification can also be performed using unsupervised Baum-Welch algorithm [13] or using error-related potential (ErrP) [14]. Figure 1: Classical \(6\times 6\) GUI matrix of P300 speller: 12 rows and columns are flashing one by one randomly. A single trial has two target intensifications out of twelve possible flashing rows and columns. By finding the intersection of the target row and column, the chosen character is extracted. Semi-supervised learning can be efficiently applied for the P300 speller using a self-training least squares support vector machine (LS-SVM) [15]. Nevertheless, generally, supervised learning algorithms provide better accuracy than semi-supervised or unsupervised methods for P300 classification. The only problem is that supervised models are usually applied for each subject separately to achieve better results. However, supervised learning algorithms can provide high performance for subject-independent training as well. For instance, a supervised learning genetic algorithm can be successfully applied for adaptive selection of the ERP wave latency for each subject [16]. Recently Riemannian geometry-based algorithms have also been used for the P300 speller owing to their robustness and transfer learning capabilities [17]. This work focuses on supervised learning models for P300 component extraction and classification. Different models, such as linear-discriminant analysis (LDA), support-vector machine (SVM), \(k\)-nearest neighbors (\(k\)NN) and convolutional neural network (CNN), are combined using an ensemble learning approach to achieve more stable results. Ensemble learning puts together the advantages of different classifiers and provides more trusted classification, which is crucial for the subject-independence of the BCI system. Apart from using ensemble learning, a different number of EEG electrodes is used in the experiments, in order to find the most subject-independent data channels for features extraction. The remainder of this paper is organized as follows. Section 2 overviews the chosen classifiers. Section 3 describes the key concepts of the proposed methodology, followed by the simulation results, presented and discussed in Section 4. Finally, the concluding remarks of the paper are presented in Section 5.