ReviewBrain sciences2015
The Human Factors and Ergonomics of P300-Based Brain-Computer Interfaces.
Review in Brain sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Direct Communication Between Brains: A Systematic PRISMA Review of Brain-To-Brain Interface.Frontiers in neurorobotics · 2021Pooled it
- A systematic review of hybrid brain-computer interfaces: Taxonomy and usability perspectives.PloS one · 2017Pooled it
- Prolonged sitting in office environments: A scoping review of assessment methods.Work (Reading, Mass.) · 2026Article
- Real-Time Mobile Robot Obstacles Detection and Avoidance Through EEG Signals.Brain sciences · 2025Article
- Filter bank temporally delayed CCA for uncalibrated SSVEP-BCI.Medical & biological engineering & computing · 2025Article
- Guiding principles and considerations for designing a well-structured curriculum for the brain-computer interface major based on the multidisciplinary nature of brain-computer interface.Frontiers in human neuroscience · 2025Review
- [An emerging major: brain-computer interface major].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Time-frequency feature extraction based on multivariable synchronization index for training-free SSVEP-based BCI.Cognitive neurodynamics · 2024Article
- Multi-information improves the performance of CCA-based SSVEP classification.Cognitive neurodynamics · 2024Article
- State-of-the-Art on Brain-Computer Interface Technology.Sensors (Basel, Switzerland) · 2023Review
- Analyzing and computing humans by means of the brain using Brain-Computer Interfaces - understanding the user - previous evidence, self-relevance and the user's self-concept as potential superordinate human factors of relevance.Frontiers in human neuroscience · 2023Review
- Usability of a Hybrid System Combining P300-Based Brain-Computer Interface and Commercial Assistive Technologies to Enhance Communication in People With Multiple Sclerosis.Frontiers in human neuroscience · 2022Article
- Advancing Brain-Computer Interface Applications for Severely Disabled Children Through a Multidisciplinary National Network: Summary of the Inaugural Pediatric BCI Canada Meeting.Frontiers in human neuroscience · 2020Article
- EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy.Brain sciences · 2019Article
- Keeping Disability in Mind: A Case Study in Implantable Brain-Computer Interface Research.Science and engineering ethics · 2018Article
- EEG-Based Brain-Computer Interfaces.Current opinion in biomedical engineering · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Individuals with severe neuromuscular impairments face many challenges in communication and manipulation of the environment. Brain-computer interfaces (BCIs) show promise in presenting real-world applications that can provide such individuals with the means to interact with the world using only brain waves. Although there has been a growing body of research in recent years, much relates only to technology, and not to technology in use-i.e., real-world assistive technology employed by users. This review examined the literature to highlight studies that implicate the human factors and ergonomics (HFE) of P300-based BCIs. We assessed 21 studies on three topics to speak directly to improving the HFE of these systems: (1) alternative signal evocation methods within the oddball paradigm; (2) environmental interventions to improve user performance and satisfaction within the constraints of current BCI systems; and (3) measures and methods of measuring user acceptance. We found that HFE is central to the performance of P300-based BCI systems, although researchers do not often make explicit this connection. Incorporation of measures of user acceptance and rigorous usability evaluations, increased engagement of disabled users as test participants, and greater realism in testing will help progress the advancement of P300-based BCI systems in assistive applications.
Indexed as
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.