ArticleBiomedical engineering online2013
Performance assessment in brain-computer interface-based augmentative and alternative communication.
Article in Biomedical engineering online, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 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
25 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Knowledge mapping and research trends of brain-computer interface technology in rehabilitation: a bibliometric analysis.Frontiers in human neuroscience · 2024Pooled it
- A systematic review of research on augmentative and alternative communication brain-computer interface systems for individuals with disabilities.Frontiers in human neuroscience · 2022Pooled it
- Brain-Computer Interfaces for Children With Complex Communication Needs and Limited Mobility: A Systematic Review.Frontiers in human neuroscience · 2021Pooled it
- Rethinking Brain-Computer Interfaces for Soft Robotic Systems: A Unified Framework and Perspective.Sensors (Basel, Switzerland) · 2026Article
- ChatBCI, a P300 speller BCI with context-driven word prediction leveraging large language models, from concept to evaluation.Scientific reports · 2026Article
- [A design and evaluation of wearable p300 brain-computer interface system based on Hololens2].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2023Article
- Recommendations for promoting user agency in the design of speech neuroprostheses.Frontiers in human neuroscience · 2023Article
- Instant classification for the spatially-coded BCI.PloS one · 2022Article
- Development of a new high sensitivity mechanical switch for augmentative and alternative communication access in people with amyotrophic lateral sclerosis.Journal of neuroengineering and rehabilitation · 2019Article
- A BCI Gaze Sensing Method Using Low Jitter Code Modulated VEP.Sensors (Basel, Switzerland) · 2019Article
- The Self-Face Paradigm Improves the Performance of the P300-Speller System.Frontiers in computational neuroscience · 2019Article
- A Novel Audiovisual P300-Speller Paradigm Based on Cross-Modal Spatial and Semantic Congruence.Frontiers in neuroscience · 2019Article
- Evaluation of Switch and Continuous Navigation Paradigms to Command a Brain-Controlled Wheelchair.Frontiers in neuroscience · 2018Article
- Analyzing text recognition from tactually evoked EEG.Cognitive neurodynamics · 2017Article
- Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors.Sensors (Basel, Switzerland) · 2017Article
- Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems.Biomedical engineering online · 2017Article
- Enhancing P300-BCI performance using latency estimation.Brain computer interfaces (Abingdon, England) · 2017Article
- What Turns Assistive into Restorative Brain-Machine Interfaces?Frontiers in neuroscience · 2016Article
- Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.Frontiers in neuroscience · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
A large number of incommensurable metrics are currently used to report the performance of brain-computer interfaces (BCI) used for augmentative and alterative communication (AAC). The lack of standard metrics precludes the comparison of different BCI-based AAC systems, hindering rapid growth and development of this technology. This paper presents a review of the metrics that have been used to report performance of BCIs used for AAC from January 2005 to January 2012. We distinguish between Level 1 metrics used to report performance at the output of the BCI Control Module, which translates brain signals into logical control output, and Level 2 metrics at the Selection Enhancement Module, which translates logical control to semantic control. We recommend that: (1) the commensurate metrics Mutual Information or Information Transfer Rate (ITR) be used to report Level 1 BCI performance, as these metrics represent information throughput, which is of interest in BCIs for AAC; 2) the BCI-Utility metric be used to report Level 2 BCI performance, as it is capable of handling all current methods of improving BCI performance; (3) these metrics should be supplemented by information specific to each unique BCI configuration; and (4) studies involving Selection Enhancement Modules should report performance at both Level 1 and Level 2 in the BCI system. Following these recommendations will enable efficient comparison between both BCI Control and Selection Enhancement Modules, accelerating research and development of BCI-based AAC systems.
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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.