Utilizing Deep Learning Towards Multi-Modal Bio-Sensing and Vision-Based Affective Computing

• Published in: IEEE transactions on affective computing Vol. 13; no. 1; pp. 96 - 107
• Main Authors: Siddharth, Jung, Tzyy-Ping, Sejnowski, Terrence J.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affect (Psychology); Affective computing; Algorithms; Arousal; bio-sensing; Brain-computer interface (BCI); Classification; computer vision; Datasets; Deep learning; ECG; EEG; Electrocardiography; Electroencephalography; emotion processing; Emotions; Face; Feature extraction; GSR; Machine learning; multi-modality; Object recognition; Performance evaluation; PPG; Support vector machines; Video data; Vision
• DOI: 10.1109/TAFFC.2019.2916015
• ISSN: 1949-3045

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.