## 5 Discussion
### Pooling Strategies and Other Studies  
The first pooling strategy, represented by pool patch \([2\times 1]\), is the most common approach used in CNN and consists of a pooling process in which the pool patch and stride are of the same size. In this study, this approach led to the lowest general accuracy rates.  
The goal of the second strategy, represented by \([3\times 1]\), is to cause overlapping of the pool patches. This strategy has been tested in previous work, although with data of a very different nature. While [24] reported no differences between performance for approaches with or without overlapping for speech tasks, we have found that, as in the work of [27], better CNN model performance can be achieved using an overlapping pool strategy.  
The third strategy, represented by pool patch \([3\times 2]\), showed that the performance of the overlapping strategy can be further enhanced by also considering data from two different adjacent channels simultaneously. This consideration is not applied to the original input, but rather to feature maps generated after the convolution patch is applied.  
Past studies involving the classification of single-trial P300 includes the work of [14], in which results are reported for P300 identification using raw data from Dataset II from the third BCI competition [17]. Rather than changing the parameters of a CNN model, they presented the results of changing the way the input is constructed. In the best scenario, they achieve accuracy rates of 0.7038 and 0.7819 for each of two subjects, with a mean accuracy of 0.7428. In the work of [29], three experiments were conducted, which compared different classifiers for classification of single-trial ERPs for rapid serial visual presentation (RSVP) tasks. They found that the best performance is achieved by a CNN, with a mean accuracy and standard deviation of \(0.86\pm 0.073\). Another RSVP task is presented in [21] where CNNs are also used for classification. By applying the CNN classifier, they found that they could improve the results obtained in previous studies. These studies are well known, but were not focused on single-trial P300 classification; however, they present approaches that inspired this work and provide a reference to what has normally been achieved in this context.  
In [25], single trial P300 classification is reported as part of their results. By using support vector machines (SVM), they achieve a mean accuracy rate of approximately 0.70 for seven subjects when considering a reduced number of EEG channels. Another case of a single-trial identification attempt comes from [30], where [PERSON]'s discriminant analysis (FDA) regularized parameters are searched for using particle swarm optimization, achieving an accuracy of 0.745 for single trials and no channel selection. These results can be fairly compared to ours (see Section 5.4), as the goal of these studies, their experimental setup and BCI approach are the same as the ones we present.  
In this study, the highest mean accuracy rate for nine subjects was 0.927, and the lowest was 0.783. The mean accuracy rates for all the models for fixed trial intervals were \(0.855\pm 0.036\), \(0.853\pm 0.031\), \(0.884\pm 0.021\) and \(0.888\pm 0.026\) for the 200-, 300-, 400- and 500-ms trial interval, respectively.