## 4 Pattern Recognition
### Restricted Boltzmann Machines
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline Train & LDA & 68 & 56 & 55 & 81 & 68 & 57 & 65 & 73 & 64 \\  & SVM & 99 & 99 & 99 & 99 & 99 & 99 & 99 & 100 & 99 & 100 \\  & BP & 67 & 55 & 54 & 81 & 68 & 57 & 65 & 72 & 65 \\  & RBM & 69 & 57 & 57 & 81 & 68 & 57 & 68 & 73 & 73 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results of the training and testing accuracy (in percentage) for nine different subjects using Linear Discriminant Analysis (LDA), Supported Vector Machines (SVM), ANN trained with Back Propagation (BP) and Restricted Boltzmann Machine (RBM) for frequency bands of (8–12) Hz and (22–30) Hz and with regression filtering over the EOG. ## 7 Discussions and Conclusions

### Discussions on the Results

**Frequency band of (8-12) Hz and no EOG filtering**: The results showed that for subject 4, 7 and 9, the SOM mapping has a better separation capacity (see Figure 14 (Subject 4), (Subject 7) and (Subject 9)) than for the rest of the subjects. Likewise, this effect is also represented on Table 2 (using Figure 12), where the winner neurons for the two classes are far apart from each other. Correspondingly, a similar effect occurs for the classification methods (Table 3), where the same subjects, plus subjects 1, 5 and 8, showed training and testing accuracy higher than chance level \(\geq\)60\(\sim\)80% for LDA, BP and RBM. However, for the other subjects, it is not easy to discriminate between both classes, which is illustrated in Figure 13, where there is no observable difference between the EEG frequency response among these subjects. It is important to notice that the SVM method did not show any discrimination capacity for any subject when applied to the testing dataset. This might be due to the kernel being too general and not working well on high dimension signals. **Frequency bands of (8-12) Hz and (22-30) Hz and EOG reduction**: Using the band reduction and the EOG regression method, the results for SOM showed now that the best separation capacity was obtained for subjects 1, 3, 6, 8 and 9. Although in the case of the classification techniques, there was a slight improvement for subjects 1, 4 and 8 in the accuracy percentage (Table 5), and there was no significant improvement over subjects 7 and 9, which already had good results on the (8-30) Hz band. In this case, it is assumed that there were some hidden attributes for subjects 1, 4 and 8 that were found due to the band reduction or filtering. However, these procedures did not necessarily help the other subjects, where the selected bands may not be optimal or the EOG contamination was not an important factor in their classification. In addition, there was no improvement over the accuracy for the subjects that already performed poorly. In

Figure 18: Times of the different methods with EOG correction. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline Test & LDA & 64 & 52 & 49 & 79 & 66 & 54 & 62 & 71 & 62 \\  & SVM & 51 & 50 & 50 & 75 & 63 & 51 & 52 & 63 & 56 \\  & BP & 66 & 52 & 52 & 80 & 66 & 55 & 61 & 71 & 62 \\  & RBM & 68 & 53 & 52 & 80 & 66 & 55 & 63 & 71 & 70 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._these cases, even the limited band and noise reduction technique could not help to uncover if there was any difference between the classes. Lastly, for the SVM method, the accuracy was low for every subject except for subjects 4, 5 and 8. In general, the accuracy of SVM highly depends on finding the correct kernel to map the function, meaning that the initial parameters introduced for mapping into a higher dimension were not optimal for this database. Furthermore, SVM usually has problems discriminating when the same parameters are used in every individual, which means that it may require a specific setup for each subject.