## 3 Results
### Discriminative MOSM-GP  
For the DMOSM-GP framework, each EEG trial is trimmed into two-seconds lasting time series to find the desired spectral relationships. Table 1 presents the absolute value of the average likelihood as the class dependency measure, depicted in Equation (12). The first column corresponds to the test condition level regarding the emotional content (valence for this experiment), columns two and three are the values obtained for the model testing, and fourth is the valid tag of the corresponding test signal. Finally, the fifth column is the predicted tag from the magnitude of the mean test likelihood. Similarly, in Table 2, the results for the DMOSM-GP strategy over the BCI database are presented. The first column corresponds to the movement associated with the experiment. Columns two and three are the average likelihoods obtained by testing the new input against each class's models. The fourth column is associated with the resulting tag. Each row in Tables 1 and 2 corresponds to a particular trial, for emotional or motor imagery experiments. From this test on complete datasets, it can be evidenced that the prediction regarding the likelihood of the trained model with the test signals is promising. The accuracy of the test data is around 73.3% for the DEAP database and about 87.33 for the BCI database. Specifically, for comparison purposes against works using the DEAP dataset, a selection of 9 subjects is performed. This selection is based on the evidence of an uncertainty test performed in [17], where the authors concluded that the subject itself did not adequately tag some experiments of the DEAP database. The scheme of implicit tagging used in this particular database allows the subjects to rate their affective result, so some of the acquired signals seem not correctly related to the emotional tests. The results reported in Table 3 show the accuracy resulting from the cross-validated DMOSM-GP scheme. The subject IDs reported are the same in the database, and some high accuracy (state-of-art comparable) ones were achieved around 78%, which is the case of subject 18. On the other hand, for the BCI database, the complete set of 9 subjects is employed, and Table 4 reports the accuracy of classification within 5 folds of DMOSM-GP. Some higher accuracy was obtained for the MI database in the subject-dependent experiments around 87% for subject ID 2. In general terms, the results associated with the BCI database are higher than the DEAP database, and it can  
Figure 6: Quantified power spectral interdependencies for two particular conditions of the motor imagery dataset. The power spectral density (PSD) has been normalized between 0–1. be related directly with the condition that is tested. In the case of emotional experiments, there is a high degree of subjectivity in the elicitation of the states. In contrast, in the case of motor imagery, the conditions are somehow more consistently replicated. \begin{table}
\begin{tabular}{c c c c c} \hline
**Level** & **Low Models** & **High Models** & **True** & **Predicted** \\ \hline
9.0 & 138.6293 & **143.0221** & A & A \\
9.0 & 10.8743 & **14.7149** & A & A \\
9.0 & **103.9478** & 100.7107 & A & B \\
9.0 & 299.0507 & **304.0222** & A & A \\
8.81 & 299.7143 & **305.0394** & A & A \\
8.56 & 363.5983 & **369.2760** & A & A \\
9.0 & 348.1999 & **353.7941** & A & A \\
9.0 & 38.8484 & **42.9119** & A & A \\
1.0 & **193.7571** & 191.0369 & B & B \\
1.0 & **145.3716** & 142.2964 & B & B \\
1.0 & 400.7168 & **406.6640** & B & A \\
1.0 & **36.0769** & 32.4378 & B & B \\
1.1 & 48.8835 & **53.0558** & B & A \\
1.0 & 132.5064 & **136.9884** & B & A \\
1.08 & **83.4116** & 80.2945 & B & B \\ \hline \multicolumn{4}{c}{One-Fold Average Accuracy 73.35 \(\pm\) 5.64\%} \\ \hline \end{tabular}
\end{table} 
Table 1: DMOSM-GP Test for brain electrical activity (BEA) analysis of emotional conditions of two classes.