Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing
This work addresses the gap in using deep learning for bio-sensing in affective computing, which could benefit researchers and applications in emotion recognition, but it appears incremental as it builds on existing datasets and methods.
The paper tackles the problem of applying deep learning to multi-modal bio-sensing and vision-based affective computing, showing that their algorithms outperform existing studies on emotion classification for datasets like DEAP and MAHNOB-HCI, with analysis on over 120 subjects and 2,800 trials.
In recent years, the use of bio-sensing signals such as electroencephalogram (EEG), electrocardiogram (ECG), etc. have garnered interest towards applications in affective computing. The parallel trend of deep-learning has led to a huge leap in performance towards solving various vision-based research problems such as object detection. Yet, these advances in deep-learning have not adequately translated into bio-sensing research. This work applies novel deep-learning-based methods to various bio-sensing and video data of four publicly available multi-modal emotion datasets. For each dataset, we first individually evaluate the emotion-classification performance obtained by each modality. We then evaluate the performance obtained by fusing the features from these modalities. We show that our algorithms outperform the results reported by other studies for emotion/valence/arousal/liking classification on DEAP and MAHNOB-HCI datasets and set up benchmarks for the newer AMIGOS and DREAMER datasets. We also evaluate the performance of our algorithms by combining the datasets and by using transfer learning to show that the proposed method overcomes the inconsistencies between the datasets. Hence, we do a thorough analysis of multi-modal affective data from more than 120 subjects and 2,800 trials. Finally, utilizing a convolution-deconvolution network, we propose a new technique towards identifying salient brain regions corresponding to various affective states.