ArticleFrontiers in human neuroscience2015
Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity.
Article in Frontiers in human neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05819047 (Assessment of Upper Limb Motor Performance Using an Interface With Haptic Feedback), which is not on this map. Cited by 48 papers.
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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.
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Assessment of Upper Limb Motor Performance Using an Interface With Haptic Feedback
Who cites it
48 citing papers in PubMed.
- Error-related potentials in EEG signals: feature-based detection for human-robot interaction.Scientific reports · 2025Article
- Real-Time Mobile Robot Obstacles Detection and Avoidance Through EEG Signals.Brain sciences · 2025Article
- Response coupling with an auxiliary neural signal for enhancing brain signal detection.Scientific reports · 2025Article
- Evaluating robotic actions: spatiotemporal brain dynamics of performance assessment in robot-assisted laparoscopic training.Frontiers in neuroergonomics · 2025Article
- Decoding the brain-machine interaction for upper limb assistive technologies: advances and challenges.Frontiers in human neuroscience · 2025Review
- Hybrid brain-computer interface using error-related potential and reinforcement learning.Frontiers in human neuroscience · 2025Article
- Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation.Scientific reports · 2024Article
- Multimodal decoding of error processing in a virtual reality flight simulation.Scientific reports · 2024Article
- Advancing passive BCIs: a feasibility study of two temporal derivative features and effect size-based feature selection in continuous online EEG-based machine error detection.Frontiers in neuroergonomics · 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
- A deep neural network and transfer learning combined method for cross-task classification of error-related potentials.Frontiers in human neuroscience · 2024Article
- EEG and EMG dataset for the detection of errors introduced by an active orthosis device.Frontiers in human neuroscience · 2024Article
- Inferring individual evaluation criteria for reaching trajectories with obstacle avoidance from EEG signals.Scientific reports · 2023Article
- Bayesian learning from multi-way EEG feedback for robot navigation and target identification.Scientific reports · 2023Article
- EEG error-related potentials encode magnitude of errors and individual perceptual thresholds.iScience · 2023Article
- Error detection and correction in intracortical brain-machine interfaces controlling two finger groups.Journal of neural engineering · 2023Article
- Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment.Frontiers in neuroergonomics · 2023Article
- Brain computer interface to distinguish between self and other related errors in human agent collaboration.Scientific reports · 2022Article
- Neuro-cognitive assessment of intentional control methods for a soft elbow exosuit using error-related potentials.Journal of neuroengineering and rehabilitation · 2022Article
Corrections and comments
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2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When a person recognizes an error during a task, an error-related potential (ErrP) can be measured as response. It has been shown that ErrPs can be automatically detected in tasks with time-discrete feedback, which is widely applied in the field of Brain-Computer Interfaces (BCIs) for error correction or adaptation. However, there are only a few studies that concentrate on ErrPs during continuous feedback. With this study, we wanted to answer three different questions: (i) Can ErrPs be measured in electroencephalography (EEG) recordings during a task with continuous cursor control? (ii) Can ErrPs be classified using machine learning methods and is it possible to discriminate errors of different origins? (iii) Can we use EEG to detect the severity of an error? To answer these questions, we recorded EEG data from 10 subjects during a video game task and investigated two different types of error (execution error, due to inaccurate feedback; outcome error, due to not achieving the goal of an action). We analyzed the recorded data to show that during the same task, different kinds of error produce different ErrP waveforms and have a different spectral response. This allows us to detect and discriminate errors of different origin in an event-locked manner. By utilizing the error-related spectral response, we show that also a continuous, asynchronous detection of errors is possible. Although the detection of error severity based on EEG was one goal of this study, we did not find any significant influence of the severity on the EEG.
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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.