[HTML][HTML] Cognitive neuroscience and robotics: Advancements and future research directions
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 …
human–system/robot interactions have been actively explored, especially in brain robotics …
Error-related potentials in reinforcement learning-based brain-machine interfaces
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 …
several studies have focused on understanding the neural correlates of error processing …
Noir: Neural signal operated intelligent robots for everyday activities
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 …
brain-robot interface system that enables humans to command robots to perform everyday …
Evaluation of lower leg muscle activities during human walking assisted by an ankle exoskeleton
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 …
human mobility and to reduce biological efforts of human locomotion. The type of assistance …
[PDF][PDF] Learning regional attention convolutional neural network for motion intention recognition based on EEG data
Z Fang, W Wang, S Ren, J Wang, W Shi, X Liang… - Proceedings of the …, 2021 - ijcai.org
Recent deep learning-based Brain-Computer Interface (BCI) decoding algorithms mainly
focus on spatial-temporal features, while failing to explicitly explore spectral information …
focus on spatial-temporal features, while failing to explicitly explore spectral information …
EEG and EMG dataset for the detection of errors introduced by an active orthosis device
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 …
motor impairments as they can integrate classical therapy approaches such as mirror …
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 …
Accelerated robot learning via human brain signals
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 …
learned. However, most RL algorithms struggle to learn in this setting since the learning …
Bayesian learning from multi-way EEG feedback for robot navigation and target identification
Many brain-computer interfaces require a high mental workload. Recent research has
shown that this could be greatly alleviated through machine learning, inferring user …
shown that this could be greatly alleviated through machine learning, inferring user …
The Augmented Intelligence Perspective on Human-in-the-Loop Reinforcement Learning: Review, Concept Designs, and Future Directions
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 …
artificial intelligence (AI) to leverage their respective strengths. While AI typically aims to …