Explainable AI aims to improve the transparency of AI decisions through interpretable methods. We explore vulnerabilities in current explanation methods using adversarial attacks and develop intuitive visualizations to enhance user trust.

BCI is a powerful way to understand users' perception directly by monitoring brain activities. We have been working on investigating effective BCI techniques using EEG and fNIRS for implicit monitoring of users' perception of given multimedia content. Here, perceptual dimensions include satisfaction, quality, immersiveness, emotion, sense of reality, etc. In particular, we focus on understanding perception for novel types of media such as 3D, high dynamic range (HDR), 4K UHD, etc. Furthermore, peripheral physiological signals such as skin temperature, skin conductance, and heart rate are also considered, which have potential to complement brain activities.

Dynamic Vision Sensors detect rapid visual changes through event-based imaging. We research algorithms for efficient interpretation of DVS data, applicable to robotics, autonomous driving, and real-time monitoring due to their speed and efficiency.

Often, heterogeneous sensors are simultaneously involved, which produce multimodal data. For instance, human actions can be recorded by cameras, depth cameras, accelerometers, and gyroscopes at the same time. They are mutually complementary, thus learning from them is beneficial to maximize performance. We have recently developed a deep learning architecture that can integrate multimodal data. It is not only effective in terms of performance but also robust against partial absence of data and modalities.