2014 |
Verma, Gyanendra Kumar; Tiwary, Uma Shanker Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals Journal Article NeuroImage, 102, Part 1 , pp. 162 - 172, 2014, ISSN: 1053-8119, (Multimodal Data Fusion). Abstract | Links | BibTeX | Tags: KNN @article{Verma2014162, title = {Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals}, author = {Gyanendra Kumar Verma and Uma Shanker Tiwary}, url = {http://www.sciencedirect.com/science/article/pii/S1053811913010999}, doi = {http://dx.doi.org/10.1016/j.neuroimage.2013.11.007}, issn = {1053-8119}, year = {2014}, date = {2014-01-01}, journal = {NeuroImage}, volume = {102, Part 1}, pages = {162 - 172}, abstract = {Abstract The purpose of this paper is twofold: (i) to investigate the emotion representation models and find out the possibility of a model with minimum number of continuous dimensions and (ii) to recognize and predict emotion from the measured physiological signals using multiresolution approach. The multimodal physiological signals are: Electroencephalogram (EEG) (32 channels) and peripheral (8 channels: Galvanic skin response (GSR), blood volume pressure, respiration pattern, skin temperature, electromyogram (EMG) and electrooculogram (EOG)) as given in the DEAP database. We have discussed the theories of emotion modeling based on i) basic emotions, ii) cognitive appraisal and physiological response approach and iii) the dimensional approach and proposed a three continuous dimensional representation model for emotions. The clustering experiment on the given valence, arousal and dominance values of various emotions has been done to validate the proposed model. A novel approach for multimodal fusion of information from a large number of channels to classify and predict emotions has also been proposed. Discrete Wavelet Transform, a classical transform for multiresolution analysis of signal has been used in this study. The experiments are performed to classify different emotions from four classifiers. The average accuracies are 81.45%, 74.37%, 57.74% and 75.94% for SVM, MLP, KNN and MMC classifiers respectively. The best accuracy is for ‘Depressing’ with 85.46% using SVM. The 32 EEG channels are considered as independent modes and features from each channel are considered with equal importance. May be some of the channel data are correlated but they may contain supplementary information. In comparison with the results given by others, the high accuracy of 85% with 13 emotions and 32 subjects from our proposed method clearly proves the potential of our multimodal fusion approach.}, note = {Multimodal Data Fusion}, keywords = {KNN}, pubstate = {published}, tppubtype = {article} } Abstract The purpose of this paper is twofold: (i) to investigate the emotion representation models and find out the possibility of a model with minimum number of continuous dimensions and (ii) to recognize and predict emotion from the measured physiological signals using multiresolution approach. The multimodal physiological signals are: Electroencephalogram (EEG) (32 channels) and peripheral (8 channels: Galvanic skin response (GSR), blood volume pressure, respiration pattern, skin temperature, electromyogram (EMG) and electrooculogram (EOG)) as given in the DEAP database. We have discussed the theories of emotion modeling based on i) basic emotions, ii) cognitive appraisal and physiological response approach and iii) the dimensional approach and proposed a three continuous dimensional representation model for emotions. The clustering experiment on the given valence, arousal and dominance values of various emotions has been done to validate the proposed model. A novel approach for multimodal fusion of information from a large number of channels to classify and predict emotions has also been proposed. Discrete Wavelet Transform, a classical transform for multiresolution analysis of signal has been used in this study. The experiments are performed to classify different emotions from four classifiers. The average accuracies are 81.45%, 74.37%, 57.74% and 75.94% for SVM, MLP, KNN and MMC classifiers respectively. The best accuracy is for ‘Depressing’ with 85.46% using SVM. The 32 EEG channels are considered as independent modes and features from each channel are considered with equal importance. May be some of the channel data are correlated but they may contain supplementary information. In comparison with the results given by others, the high accuracy of 85% with 13 emotions and 32 subjects from our proposed method clearly proves the potential of our multimodal fusion approach. |
Publications
2014 |
Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals Journal Article NeuroImage, 102, Part 1 , pp. 162 - 172, 2014, ISSN: 1053-8119, (Multimodal Data Fusion). |