ReviewFrontiers in robotics and AI2020
Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction.
Review in Frontiers in robotics and AI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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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Who cites it
19 citing papers in PubMed, 83 citations in OpenAlex.
- Rethinking Brain-Computer Interfaces for Soft Robotic Systems: A Unified Framework and Perspective.Sensors (Basel, Switzerland) · 2026Article
- Symbiotic brain-machine drawing via visual brain-computer interfaces.npj biomedical innovations · 2026Article
- Bridging cognition and control through passive eye movement integration in motor imagery brain-computer interfaces.Frontiers in human neuroscience · 2026Review
- Do We View Robots as We Do Ourselves? Examining Robotic Face Processing Using EEG.Brain sciences · 2025Article
- Integrating active brain-computer interfaces (aBCIs) with passive BCIs (pBCIs) under different frustration levels.Scientific reports · 2025Article
- Real-Time Mobile Robot Obstacles Detection and Avoidance Through EEG Signals.Brain sciences · 2025Article
- Robots as Mental Health Coaches: A Study of Emotional Responses to Technology-Assisted Stress Management Tasks Using Physiological Signals.Sensors (Basel, Switzerland) · 2024Article
- Decoding the Debate: A Comparative Study of Brain-Computer Interface and Neurofeedback.Applied psychophysiology and biofeedback · 2024Review
- A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing.Sensors (Basel, Switzerland) · 2023Article
- Driving drowsiness detection using spectral signatures of EEG-based neurophysiology.Frontiers in physiology · 2023Article
- Article
- Classifying human emotions in HRI: applying global optimization model to EEG brain signals.Frontiers in neurorobotics · 2023Article
- Social robots as effective language tutors for children: empirical evidence from neuroscience.Frontiers in neurorobotics · 2023Article
- Integration of Augmented Reality and Brain-Computer Interface Technologies for Health Care Applications: Exploratory and Prototyping Study.JMIR formative research · 2022Article
- Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data.Neurophotonics · 2022Article
- Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces.Frontiers in human neuroscience · 2022Article
- Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review.Frontiers in artificial intelligence · 2022Review
- Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications.Sensors (Basel, Switzerland) · 2021Review
- A simulated experiment to explore robotic dialogue strategies for people with dementia.Journal of rehabilitation and assistive technologies engineeringArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-computer interfaces (BCIs) have long been seen as control interfaces that translate changes in brain activity, produced either by means of a volitional modulation or in response to an external stimulation. However, recent trends in the BCI and neurofeedback research highlight passive monitoring of a user's brain activity in order to estimate cognitive load, attention level, perceived errors and emotions. Extraction of such higher order information from brain signals is seen as a gateway for facilitation of interaction between humans and intelligent systems. Particularly in the field of robotics, passive BCIs provide a promising channel for prediction of user's cognitive and affective state for development of a user-adaptive interaction. In this paper, we first illustrate the state of the art in passive BCI technology and then provide examples of BCI employment in human-robot interaction (HRI). We finally discuss the prospects and challenges in integration of passive BCIs in socially demanding HRI settings. This work intends to inform HRI community of the opportunities offered by passive BCI systems for enhancement of human-robot interaction while recognizing potential pitfalls.
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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.