# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
###### Abstract  
Classification of brain signal features is a crucial process for any brain-computer interface (BCI) device, including speller systems. The positive P300 component of visual event-related potentials (ERPs) used in BCI spellers has individual variations of amplitude and latency that further changes with brain abnormalities such as amyotrophic lateral sclerosis (ALS). This leads to the necessity for the users to train the speller themselves, which is a very time-consuming procedure. To achieve subject-independence in a P300 speller, ensemble classifiers are proposed based on classical machine learning models, such as the support vector machine (SVM), linear discriminant analysis (LDA), k-nearest neighbors (kNN), and the convolutional neural network (CNN). The proposed voters were trained on healthy subjects' data using a generic training approach. Different combinations of electroencephalography (EEG) channels were used for the experiments presented, resulting in single-channel, four-channel, and eight-channel classification. ALS patients' data represented robust results, achieving more than 90% accuracy when using an ensemble of LDA, kNN, and SVM on four active EEG channels data in the occipital area of the brain. The results provided by the proposed ensemble voting models were on average about 5% more accurate than the results provided by the standalone classifiers. The proposed ensemble models could also outperform boosting algorithms in terms of computational complexity or accuracy. The proposed methodology shows the ability to be subject-independent, which means that the system trained on healthy subjects can be efficiently used for ALS patients. Applying this methodology for online speller systems removes the necessity to retrain the P300 speller. brain-computer interface; EEG classification; ensemble learning; P300 speller +
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to communicate with the outer world. One of the most common paralyzing diseases is amyotrophic lateral sclerosis (ALS), which paralyzes the whole organism, destroying a human's ability to speak and communicate. According to statistics, more than six thousand people are diagnosed with ALS each year all over the world [8]. BCI spellers commonly use the event-related potential (ERP) paradigm, which states that a human's reaction to a stimulus can be classified by analyzing voltage deflections of EEG signals, called ERP components. ERP events can be classified into several groups which are: slow cortical potential (SCP), neuronal potential, event-related synchronization (ERS), event-related desynchronization (ERD), and visual evoked potentials. SCP is caused by the shifts in the depolarization levels of dendrites, while the neuronal potential is caused by the change of neuronal firing [9]. ERS can be indicated by an increase in power in some frequency bands of the EEG signal, while ERD is characterized by the decrease in power in that frequency. The most popular ERP type used for speller systems is visual evoked ERP. Visual evoked ERP components are indicated by the latency and sign of the voltage amplitude. ERP components are used in the oddball paradigm, in which repetitive stimuli are presented to the subject. High-probability nontarget visual stimuli are mixed with low-probability target stimuli when the oddball paradigm is applied. In speller systems, the target stimulus is the intensification of the chosen character. The oddball paradigm is used in the classical P300 speller [10], which identifies the target symbol by extracting the positive voltage peak, starting at about 300 ms after the oddball stimulus, called P300 component [11]. Figure 1 shows the graphical user interface (GUI) of a P300 speller for an English-speaking user, which is usually presented as a \(6\times 6\) matrix of symbols, having 12 possible intensifications of 12 rows/columns. By analyzing the EEG signal's ERP response, 2 target intensifications can be detected out of 12: one row and one column intensification.