ReviewFrontiers in human neuroscience2022
Error-Related Potentials in Reinforcement Learning-Based Brain-Machine Interfaces.
Review in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Characterization of error-related potentials during the command of a lower-limb exoskeleton based on deep learning.Journal of neuroengineering and rehabilitation · 2025Article
- Advancing Brain-Computer Interface Closed-Loop Systems for Neurorehabilitation: Systematic Review of AI and Machine Learning Innovations in Biomedical Engineering.JMIR biomedical engineering · 2025Review
- Differential motor neuron activity in rats during successful and failed grasping.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Error-related potentials during multitasking involving sensorimotor control: an ERP and offline decoding study for brain-computer interface.Frontiers in human neuroscience · 2025Article
- Hybrid brain-computer interface using error-related potential and reinforcement learning.Frontiers in human neuroscience · 2025Article
- A deep neural network and transfer learning combined method for cross-task classification of error-related potentials.Frontiers in human neuroscience · 2024Article
- Yes or no? A study of ErrPs in the "guess what I am thinking" paradigm with stimuli of different visual content.Frontiers in psychology · 2024Article
- Human in the collaborative loop: a strategy for integrating human activity recognition and non-invasive brain-machine interfaces to control collaborative robots.Frontiers in neurorobotics · 2024Review
- A generic error-related potential classifier based on simulated subjects.Frontiers in human neuroscience · 2024Article
- Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors.Biosensors · 2023Article
- Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment.Frontiers in neuroergonomics · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, advances in brain-machine interface systems using non-invasive techniques further enabled the use of the measured signals in different applications. The possibility of detecting these error-related potentials (ErrPs) under different experimental setups on a single-trial basis has further increased interest in their integration in closed-loop settings to improve system performance, for example, by performing error correction. Fewer works have, however, aimed at reducing future mistakes or learning. We present a review focused on the current literature using non-invasive systems that have combined the ErrPs information specifically in a reinforcement learning framework to go beyond error correction and have used these signals for learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.