Advances in brain science and computer technology in the past decade have led to exciting
developments in brain–computer interface (BCI), thereby making BCI a top research area in …

Electroencephalography (EEG) has been a staple method for identifying certain health
conditions in patients since its discovery. Due to the many different types of classifiers …

EEG-based brain-controlled mobile robots can serve as powerful aids for severely disabled
people in their daily life, especially to help them move voluntarily. In this paper, we provide a …

The ability to recognize errors is crucial for efficient behavior. Numerous studies have
identified electrophysiological correlates of error recognition in the human brain (error …

Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in
dynamic environments based on feedback. Explicit human feedback during robot RL is …

The present disclosure relates to systems and methods for providing a hybrid brain-
computer-interface (hBCI) that can detect an individual's reinforcement signals (eg, level of …

We focus on enabling robots to proactively assist humans in assembly tasks by adapting to
their preferred sequence of actions. Much work on robot adaptation requires human …

The intelligent recognition of electroencephalogram (EEG) signals has become an important
approach to the detection of epilepsy. Among existing intelligent identification methods …

Due to the non-stationary nature of electroencephalography (EEG) signals, a Brain-
computer Interfacing (BCI) system requires frequent calibration. This leads to inter session …

Abstract Providing Reinforcement Learning (RL) agents with human feedback can
dramatically improve various aspects of learning. However, previous methods require …