## 4 Implementation and Results
### Emotion Classification and Performance Evaluation
No-correlation selected the following time features as non-redundant: mean, standard deviation, variance, Shannon entropy, energy, Higuchi, higher-order crossing, Hjorth mobility, and Hjorth complexity. In the same way, in the frequency domain, the selected features were the PSD of the five frequency bands: delta (0.5 to 4 Hz), theta (4 to 8 Hz), alpha (8 to 13 Hz), beta (13 to 30 Hz), and gamma (over 30 Hz), plus the power ratio, power spectral density for all range of frequencies, and fast Fourier transform. The selected features were the same as the originals in the time-frequency domain. In the location domains, the chosen features were only DASM; the RASM was eliminated for being correlated to the first ones. It is worth noting that these features were calculated between the pairs of signals located on each side of the scalp (16 pairs for 32 channels). Table 5 presents an exhaustive evaluation of feature extraction methods and machine learning algorithms for emotion recognition in the valence-arousal space using the DEAP dataset. We explore time, frequency, time-frequency, and location (spatial)-domain features that measure asymmetry between the right and left brain regions. The performance of three machine learning algorithms, SVM, KNN, and ANN, are reported for each feature set and their combinations to monitor performance. \begin{table}
\begin{tabular}{l l c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Domain**}} & \multicolumn{3}{c}{**Accuracy**} \\ \cline{3-5}  & **SVM** & **KNN** & **ANN** \\ \hline \multirow{2}{*}{Time domain} & 21 & 0.70 & 0.71 & 0.76 \\ \cline{2-5}  & Selected 9 & 0.75 & 0.78 & 0.80 \\ \hline \multirow{2}{*}{Frequency domain} & 11 (included 5 bands) & 0.80 & 0.77 & 0.82 \\ \cline{2-5}  & 7 (included 5 bands) & 0.81 & 0.81 & 0.88 \\ \hline \multirow{2}{*}{Time–frequency domain} & 2 & 0.86 & 0.83 & 0.90 \\ \hline \multirow{2}{*}{Location domain} & 16 pairs for 32 channels for DASM and 16 pairs for 32 channels for RASM & 0.60 & 0.62 & 0.75 \\ \cline{2-5}  & Selected 16 pairs for 32 channels for DASM & 0.64 & 0.64 & 0.81 \\ \hline \multirow{6}{*}{Hybrid (combination of selected features in the domains)} & 9 time + 7 frequency & 0.87 & 0.82 & 0.95 \\ \cline{2-5}  & 9 time + 2 time–frequency & 0.78 & 0.82 & 0.89 \\ \cline{2-5}  & 9 time + 16 location & 0.70 & 0.74 & 0.86 \\ \cline{2-5}  & 3 frequency  
In the time domain with the 21 features described in Table 1, the results for accuracy were 0.70 for SVM, 0.71 for KNN, and 0.76 for ANN. A correlation matrix was applied to eliminate characteristics with redundant information that could add noise to the classification. When using the selected nine features in the time domain, the performance increased substantially, showing accuracies of 0.75, 0.78, and 0.80 for SVM, KNN, and ANN, respectively. Using all the described features in the frequency domain (Table 1), the results were 0.80 for SVM, 0.77 for KNN, and 0.82 for ANN. Applying feature selection, we obtained seven relevant features and, again, the accuracy values improved to 0.81, 0.81, and 0.88 for SVM, KNN, and ANN. This last algorithm showed the best performance. In the time-frequency domain, the original two features were used and yielded accuracies of 0.86 for SVM, 0.83 for KNN, and 0.90 for ANN. The location domain was calculated using the asymmetries on the right and left of the brain (DASM and RASM). With the original features, the accuracies were 0.60 for SVM, 0.62 for KNN, and 0.75 for ANN. Using a correlation matrix, the selected characteristics were DASM (16 pairs), and ANN's performance shot up to 0.81. The tests with hybrid models that were conducted combining features from multiple domains yielded the following best-performing models: nine time plus seven frequency features: ANN 0.95; nine time plus seven frequency plus two time-frequency features: ANN 0.94; nine time plus seven frequency plus two time-frequency + sixteen location features: ANN 0.96.