Selection of stable features for modeling 4-D affective space from EEG recording
Abstract
Recent advances in neuroscience made it possible to understand how the human brain processes emotions and affective states. However, the modeling of emotion remains elusive due to inherent ambiguity and complexity related to the perception of emotions, interpersonal variabilities, and context-specific interpretations. Here, we present a robust method of modeling 4-D continuous affective space (Valence, Arousal, Like, and Dominance). First, we determined the functional areas and frequency bands related to 4-D affective space. Second, we extracted and selected a set of stable features. For both steps, we used two different feature selection methods namely: Recursive Feature Elimination (RFE) and stability selection method. Moreover, compare their performances. For the RFE, we used Random Forest (RF), Support Vector Regression (SVR), Tree-based bagging, and for the stability selection, we used Randomized Lasso as an estimator. Empirical analysis on the DEAP data set shows that the stability selection method consistently provides relevant set of bands, electrode location and features over a range of model parameters. We also observed that only a small number of locations (40%-63%) and certain frequency bands specifically, gamma band frequency over Superior Temporal Gyrus, Supramarginal Gyrus, and Somatosensory Association Cortex were the highest ranked features across the affective dimensions. The selected features using the stability criteria were used to model 4-D affective space using SVR. Empirical analyses shows that the Root Mean Square Error (RMSE) for Valence, Arousal, Dominance, and Like are 2.13, 2.00, 2.07, and 2.11 respectively. In addition, we also compare the performance of this method with feature fusion and ensemble classification. It was observed that the SVR with selected features outperformed all other approaches. The predicted Valence-Arousal-Dominance were converted to categorical emotions for seamless interpretation.
Publication Title
Proceedings of the International Joint Conference on Neural Networks
Recommended Citation
Al-Fahad, R., Yeasin, M., Anam, A., & Elahian, B. (2017). Selection of stable features for modeling 4-D affective space from EEG recording. Proceedings of the International Joint Conference on Neural Networks, 1202-1209. https://doi.org/10.1109/IJCNN.2017.7965989
