Beyond supervised learning for pervasive healthcare
The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
healthcare and medical practice. However, inherent limitations in healthcare data, namely …
Wearable EEG electronics for a Brain–AI Closed-Loop System to enhance autonomous machine decision-making
Human nonverbal communication tools are very ambiguous and difficult to transfer to
machines or artificial intelligence (AI). If the AI understands the mental state behind a user's …
machines or artificial intelligence (AI). If the AI understands the mental state behind a user's …
Single trial detection of error-related potentials in brain–machine interfaces: a survey and comparison of methods
Objective. Error-related potential (ErrP) is a potential elicited in the brain when humans
perceive an error. ErrPs have been researched in a variety of contexts, such as to increase …
perceive an error. ErrPs have been researched in a variety of contexts, such as to increase …
Location-Aware Encoding for Lesion Detection in Ga-DOTATATE Positron Emission Tomography Images
Objective: Lesion detection with positron emission tomography (PET) imaging is critical for
tumor staging, treatment planning, and advancing novel therapies to improve patient …
tumor staging, treatment planning, and advancing novel therapies to improve patient …
Crew: Facilitating human-ai teaming research
With the increasing deployment of artificial intelligence (AI) technologies, the potential of
humans working with AI agents has been growing at a great speed. Human-AI teaming is an …
humans working with AI agents has been growing at a great speed. Human-AI teaming is an …
Error-related potential-based shared autonomy via deep recurrent reinforcement learning
Objective. Error-related potential (ErrP)-based brain–computer interfaces (BCIs) have
received a considerable amount of attention in the human–robot interaction community. In …
received a considerable amount of attention in the human–robot interaction community. In …
Interaction-grounded learning with action-inclusive feedback
Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's
goal is to optimally interact with the environment with no explicit reward to ground its …
goal is to optimally interact with the environment with no explicit reward to ground its …
Towards interactive reinforcement learning with intrinsic feedback
Reinforcement learning (RL) and brain–computer interfaces (BCI) have experienced
significant growth over the past decade. With rising interest in human-in-the-loop (HITL) …
significant growth over the past decade. With rising interest in human-in-the-loop (HITL) …
ParaDC: Parallel-learning-based dynamometer cards augmentation with diffusion models in sucker rod pump systems
The accurate fault diagnosis of sucker rod pump systems (SRPs) is crucial for the
sustainable development of oil & gas. Currently, dynamometer cards (DCs) are widely …
sustainable development of oil & gas. Currently, dynamometer cards (DCs) are widely …
A deep neural network and transfer learning combined method for cross-task classification of error-related potentials
Background Error-related potentials (ErrPs) are electrophysiological responses that
naturally occur when humans perceive wrongdoing or encounter unexpected events. It …
naturally occur when humans perceive wrongdoing or encounter unexpected events. It …