ArticleScientific reports2019
Online asynchronous decoding of error-related potentials during the continuous control of a robot.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Closed-loop error damping in human BCI using pre-error motor cortex activity.bioRxiv : the preprint server for biology · 2026Article
- Characterization of error-related potentials during the command of a lower-limb exoskeleton based on deep learning.Journal of neuroengineering and rehabilitation · 2025Article
- Error-related potentials in EEG signals: feature-based detection for human-robot interaction.Scientific reports · 2025Article
- Detecting unacceptable behavior of an autonomous vehicle using electroencephalography.Scientific reports · 2025Article
- Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges.Frontiers in human neuroscience · 2025Review
- Multimodal decoding of error processing in a virtual reality flight simulation.Scientific reports · 2024Article
- Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing.Scientific reports · 2024Article
- A deep neural network and transfer learning combined method for cross-task classification of error-related potentials.Frontiers in human neuroscience · 2024Article
- Inferring individual evaluation criteria for reaching trajectories with obstacle avoidance from EEG signals.Scientific reports · 2023Article
- EEG error-related potentials encode magnitude of errors and individual perceptual thresholds.iScience · 2023Article
- Unsupervised adaptation of an ECoG based brain-computer interface using neural correlates of task performance.Scientific reports · 2022Article
- A human-in-the-loop approach for enhancing mobile robot navigation in presence of obstacles not detected by the sensory set.Frontiers in robotics and AI · 2022Article
- Error-Related Potentials in Reinforcement Learning-Based Brain-Machine Interfaces.Frontiers in human neuroscience · 2022Review
- Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control.Frontiers in human neuroscience · 2022Review
- Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials.Communications biology · 2021Article
- Classification of error-related potentials from single-trial EEG in association with executed and imagined movements: a feature and classifier investigation.Medical & biological engineering & computing · 2020Article
- Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction.Frontiers in robotics and AI · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Error-related potentials (ErrPs) are the neural signature of error processing. Therefore, the detection of ErrPs is an intuitive approach to improve the performance of brain-computer interfaces (BCIs). The incorporation of ErrPs in discrete BCIs is well established but the study of asynchronous detection of ErrPs is still in its early stages. Here we show the feasibility of asynchronously decoding ErrPs in an online scenario. For that, we measured EEG in 15 participants while they controlled a robotic arm towards a target using their right hand. In 30% of the trials, the control of the robotic arm was halted at an unexpected moment (error onset) in order to trigger error-related potentials. When an ErrP was detected after the error onset, participants regained the control of the robot and could finish the trial. Regarding the asynchronous classification in the online scenario, we obtained an average true positive rate (TPR) of 70% and an average true negative rate (TNR) of 86.8%. These results indicate that the online asynchronous decoding of ErrPs was, on average, reliable, showing the feasibility of the asynchronous decoding of ErrPs in an online scenario.
Identifiers
What OpenQuestion holds
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.