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In recent years, the use of bio-sensing signals such as electroencephalogram(EEG), electrocardiogram (ECG), etc. have garnered interest towardsapplications in affective computing. The parallel trend of deep-learning hasled to a huge leap in performance towards solving various vision-based researchproblems such as object detection. Yet, these advances in deep-learning havenot adequately translated into bio-sensing research. This work applies noveldeep-learning-based methods to various bio-sensing and video data of fourpublicly available multi-modal emotion datasets. For each dataset, we firstindividually evaluate the emotion-classification performance obtained by eachmodality. We then evaluate the performance obtained by fusing the features fromthese modalities. We show that our algorithms outperform the results reportedby other studies for emotion/valence/arousal/liking classification on DEAP andMAHNOB-HCI datasets and set up benchmarks for the newer AMIGOS and DREAMERdatasets. We also evaluate the performance of our algorithms by combining thedatasets and by using transfer learning to show that the proposed methodovercomes the inconsistencies between the datasets. Hence, we do a thoroughanalysis of multi-modal affective data from more than 120 subjects and 2,800trials. Finally, utilizing a convolution-deconvolution network, we propose anew technique towards identifying salient brain regions corresponding tovarious affective states.

Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing