Honggu Lee

Taeheon Lee, Jeonghwan Hwang, Honggu Lee

Neural networks often obtain sub-optimal representations during training, which degrade robustness as well as classification performances. This is a severe problem in applying deep learning to bio-medical domains, since models are vulnerable to being harmed by irregularities and scarcities in data. In this study, we propose a pre-training technique that handles this challenge in sleep staging tasks. Inspired by conventional methods that experienced physicians have used to classify sleep states from the existence of characteristic waveform shapes, or template patterns, our method introduces a cosine similarity based convolutional neural network to extract representative waveforms from training data. Afterwards, these features guide a model to construct representations based on template patterns. Through extensive experiments, we demonstrated that guiding a neural network with template patterns is an effective approach for sleep staging, since (1) classification performances are significantly enhanced and (2) robustness in several aspects are improved. Last but not least, interpretations on models showed that notable features exploited by trained experts are correctly addressed during prediction in the proposed method.

Jaehyun Nam, Hyesun Chung, Young ah Seong et al.

Emotion recognition technology is crucial in providing a personalized user experience. It is especially important in virtual reality(VR) to assess the user's emotions to enhance their sense of immersion. We propose an emotion recognition interface that incorporates the user's biometric data with machine learning technology for increasing user engagement in VR. Our key technologies include brainwave sensors and eye-tracking cameras embedded in a VR headset, which seamlessly acquire physiological signals, and secondly, an attractiveness recognition algorithm that uses bio-signals to predict the user's attraction on visual stimuli. We conducted experiments to test the performance of the system, and also interviewed experts and participants to acquire opinions on the system. This study demonstrated the technical feasibility of our system with high accuracy and usability. Interviewees expected that the interface will be actively used in the context of various applications. Our proposed interface could contribute to an immersive VR experience design.