In recent years, brain-based technologies that capitalise on human abilities to facilitate
human–system/robot interactions have been actively explored, especially in brain robotics …

The human brain has been an object of extensive investigation in different fields. While
several studies have focused on understanding the neural correlates of error processing …

We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent
brain-robot interface system that enables humans to command robots to perform everyday …

Wearable robots like ankle exoskeletons have demonstrated the capability to enhance
human mobility and to reduce biological efforts of human locomotion. The type of assistance …

Recent deep learning-based Brain-Computer Interface (BCI) decoding algorithms mainly
focus on spatial-temporal features, while failing to explicitly explore spectral information …

Exoskeletons and orthoses are frequently used to facilitate limb movements in humans with
motor impairments as they can integrate classical therapy approaches such as mirror …

In reinforcement learning (RL), sparse rewards are a natural way to specify the task to be
learned. However, most RL algorithms struggle to learn in this setting since the 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 …

Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to
autonomously learn complex tasks. While sparsity is a natural way to define a reward in …

Augmented intelligence (AuI) is a concept that combines human intelligence (HI) and
artificial intelligence (AI) to leverage their respective strengths. While AI typically aims to …