# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
## 4 Simulation Results
### Experimental Settings  
The experimental validation of the proposed models was verified on two datasets [31; 32]. The datasets were obtained 10-10 EEG electrode positions with modified combinatorial nomenclature [36]. The datasets used a unipolar reference [37]. The signal was referenced to the ground earlobe and grounded to the left mastoid. In order to have the same number of electrodes, two electrodes (C3, C4) were removed from the Akimpech data. As a result eight-channel (Fz, Cz, Pz, P3, P4, PO7, PO8, Oz) data were extracted from both datasets. The provided EEG values vectors \(X\) were marked with a \(y\) label. For EEG vectors containing target P300 peak \(y=1\), while for nontarget flashings \(y=-1\). The raw EEG data of healthy subjects were filtered using a Chebyshev 4 th order notch filter for the frequency range of 58-62 Hz anda Chebyshev 8 th order band-pass filter for 0.1-60 Hz range by the authors of the dataset. The frequencies higher than 30 Hz or \(\gamma\)-band of EEG signal did not need to be considered in the oddball paradigm. Thus, the EEG signal was again band-passed using the 0.1-30 Hz frequency range. The frequencies below 30 Hz, which are \(\alpha\)-band (8-13 Hz), \(\beta\)-band (13-30 Hz), \(\delta\)-band (0.1-4 Hz) and \(\theta\)-band (4-8 Hz) brainwave frequencies, were mainly considered for P300 component extraction. The data of ALS patients were already band-passed for 0.1-30 Hz range by the dataset providers. Some researchers prefer using 1000 ms latency of EEG vectors for classifying P300 component, for instance the time period from \(-200\) ms to 800 ms can be considered as in [20]. The period starting from 0 ms to 700 ms is also a popular choice for P300 detection [38]. To reduce the redundancy of the dataset, it was decided not to consider the whole 1000 ms time period for each flashing but only to take the period up to 700 ms after the stimuli. However, since the dataset considered not only healthy subjects but also ALS patients, it was decided to extend the period taking into consideration 100 ms before the stimulus. This can improve the classification, as a sharper difference can be detected between the voltage detected 100 ms before the stimulus and 300 ms after the stimulus, rather than the difference between the beginning of the stimulus (0 ms) and the P300 component. Thus, it was decided to consider the regions starting from \(-100\) ms before the flashing and ending with the 700 ms after the flashing. By taking the \(-100\) ms to 700 ms latency period, 204 data points were extracted for each flashing trial, and the sampling rate was 256 Hz. The removal of the unnecessary EEG data can improve the computational complexity of the ensemble models, which require more computational resources than classical standalone classifiers. In addition, the dataset was balanced by removing redundant nontarget EEG vectors. Initially, there were 25 letters of input provided by the dataset for each subject, which gave 300 data samples for a single subject. There were 250 nontarget data samples out of 300. In order to balance the data, only 75 of them were randomly selected for further training. The dataset comprised 60% of the nontarget class and 40% of the target class data from data balancing steps. This gave us 125 data samples for each subject. Training data was collected from 8 healthy subjects, resulting in 1000 data samples. Test data consisted of 500 data samples of healthy subjects and 625 data samples from ALS patients. Instead of complex dimensionality reduction techniques, such as principal component analysis (PCA), the EEG signal was averaged by the channels for its further classification by LDA, \(k\)NN, and SVM. The proposed models were trained on 1000 data samples and tested for 500 data samples of healthy subjects. In order to evaluate the models, 3-fold validation was performed. Each model was trained and validated three times and the average metrics were calculated for healthy subjects' training. The trained models were then tested on 625 data samples of ALS patients.