## 4 Experimental Results
### Classification  
In Figure 9, the distribution of the classifier performance is presented for all participants and data folds, in form of box plots (where top and bottom box plot edges represent the 75 and 25 percentiles, whiskers show the min and max values, the median is indicated by the horizontal red line and the mean by the black asterisk). The statistical significance is represented above each box plot, where '**' marks significance at 0.01 and '**' at 0.05. The chance level of 33%, such as for 3 classes, is highlighted by the horizontal dotted line.  
Figure 8: GA Power Spectrum over all blocks, for Uni (blue), Nat (red) and Frac images (orange), for all channels, on: (a) 3–40 Hz, (b) 5–12 Hz, (c) 15–35 Hz.  
Considering multi-class classification on the temporal features, the average classification performance is 49%, significantly over chance level (given by \(t\)-test at \(\alpha\) = 0.0001), with the lowest mean accuracies for participants P3 and P4: Acc(P3) = 39.3% and Acc(P4) = 43.3% (as seen in Figure 9). For the remaining participants the performances exceed 50%, statistically significant with \(t\)-test at 5% significance level, and even at 1% significance level for P7 and P8 with highest performances over all folds.  
When analyzing the performance within classes, very good performances are likewise obtained in each case, with a mean 52.81% accuracy for multi-class classification. The confusion matrix of last classification fold is presented in Table 1, where the rows relate to the target class, and the columns to the classifier output). The diagonal shows the percentage of correct classified epochs in each class (55.3% for Uni, 53.5% for Nat and 49.6% for Frac), statistically significant over the chance level of 33.33%. The miss-classified trials for Nat and Frac classes (25.1-26.9%) tend to be higher then for Uni class (21.5-23.5%), which might relate to the higher amplitude difference within trials between Nat or Frac classes, as compared to Uni, and smaller between Nat and Frac classes; even though, the percentage difference between miss-classifications is not significant to support this conclusion.  
In addition, since the differences obtained in the spectral domain bring additional information (as shown in [30; 76]), we have performed also multi-modal classification integrating also spectral features in addition to temporal features, in a complementary scenario, considering temporal features (from P3, P4) and spectral features (from T3, T4). However, the classification performance did not improve significantly: 52.46% for Uni, 54.21% for Nat, 53.24% for Frac (ns. with \(t\)-test at \(\alpha\) = 0.05, \(p\) = 0.5324, \(p\) = 0.8817, \(p\) = 0.2872). The effect of no considerable improved performance might be due to the fact that compared to the temporal case, smaller differentiations are observed between classes in the spectral domain (<1 dB) which does not improve much the classification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Output**} \\  & & **Uni** & **Nat** & **Frac** \\ \hline \multirow{3}{*}{**Target**} & **Uni** & **55.3** & 21.7 & 23 \\  & **Nat** & 21.5 & **53.5** & 25.1 \\ \cline{1-1}  & **Frac** & 23.5 & 26.9 & **49.6** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Normalized mean confusion matrix (in %) for multi-class classification on the last fold.  
Figure 9: Multi-class classification performance (Uni, Nat, Frac) for each participant, considering the three folds. The box-plots consider the 25% and 75% percentiles, the the black asterisk represents the mean values and the red horizontal line is the median.