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

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 researc...