Related papers: EEG-Based Analysis of Brain Responses in Multi-Mod…

Understanding the interaction of neural and cardiac systems during cognitive activity is critical to advancing physiological computing. Although EEG has been the gold standard for assessing mental workload, its limited portability restricts…

We propose MetaEMG, a meta-learning approach for fast adaptation in intent inferral on a robotic hand orthosis for stroke. One key challenge in machine learning for assistive and rehabilitative robotics with disabled-bodied subjects is the…

This paper explores the concept of engagement, the process by which individuals in an interaction start, maintain and end their perceived connection to one another. The paper reports on one aspect of engagement among human interactors--the…

Due to the potentially large number of units involved, the interaction with a multi-robot system is likely to exceed the limits of the span of apprehension of any individual human operator. In previous work, we studied how this issue can be…

Affect understanding capability is essential for social robots to autonomously interact with a group of users in an intuitive and reciprocal way. However, the challenge of multi-person affect understanding comes from not only the accurate…

This paper tackles the challenging task of evaluating socially situated conversational robots and presents a novel objective evaluation approach that relies on multimodal user behaviors. In this study, our main focus is on assessing the…

Museums increasingly rely on digital content to support visitors' understanding of artworks, yet little is known about how these formats shape the emotional engagement that underlies meaningful art experiences. This research presents an…

How human-like do conversational robots need to look to enable long-term human-robot conversation? One essential aspect of long-term interaction is a human's ability to adapt to the varying degrees of a conversational partner's engagement…

Predicting and understanding the changes in cognitive performance, especially after a longitudinal intervention, is a fundamental goal in neuroscience. Longitudinal brain stimulation-based interventions like transcranial direct current…

Emotion estimation in music listening is confronting challenges to capture the emotion variation of listeners. Recent years have witnessed attempts to exploit multimodality fusing information from musical contents and physiological signals…

In recent years, the demand for social robots has grown, requiring them to adapt their behaviors based on users' states. Accurately assessing user experience (UX) in human-robot interaction (HRI) is crucial for achieving this adaptability.…

For social robots to maintain long-term engagement as exercise instructors, rapport-building is essential. Motor mimicry--imitating one's physical actions--during social interaction has long been recognized as a powerful tool for fostering…

Communication delays in mobile robot teleoperation adversely affect human-machine collaboration. Understanding delay effects on human operational performance and neurocognition is essential for resolving this issue. However, no previous…

In this study, we introduce an innovative EEG signal reconstruction sub-module designed to enhance the performance of deep learning models on EEG eye-tracking tasks. This sub-module can integrate with all Encoder-Classifier-based deep…

In today's world, many patients with cognitive impairments and motor dysfunction seek the attention of experts to perform specific conventional therapies to improve their situation. However, due to a lack of neurorehabilitation…

Collaborative learning is an educational approach that enhances learning through shared goals and working together. Interaction and regulation are two essential factors related to the success of collaborative learning. Since the information…

Physical interactive robotics, ranging from wearable devices to collaborative humanoid robots, require close coordination between mechanical design and control. However, evaluating interactive dynamics is challenging due to complex human…

Purpose: Mutual acceptance is required for any human-to-human interaction. Therefore, one would assume that this also holds for robot-patient interactions. However, the medical robotic imaging field lacks research in the area of acceptance.…

Robot assistants for older adults and people with disabilities need to interact with their users in collaborative tasks. The core component of these systems is an interaction manager whose job is to observe and assess the task, and infer…

Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnosis and brain-computer interfaces (BCI). The integration of multimodal data has been shown to…