ArticleBrain sciences2020
Brain-Computer Interfaces: Toward a Daily Life Employment.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Investigating the cortical effect of false positive feedback on motor learning in motor imagery based rehabilitative BCI training.Journal of neuroengineering and rehabilitation · 2025Article
- Toward brain-computer interface speller with movement-related cortical potentials as control signals.Frontiers in human neuroscience · 2025Article
- Advancements in brain-machine interfaces for application in the metaverse.Frontiers in neuroscience · 2024Review
- Brain-computer interface: trend, challenges, and threats.Brain informatics · 2023Review
- JUNO Project: Deployment and Validation of a Low-Cost Cloud-Based Robotic Platform for Reliable Smart Navigation and Natural Interaction with Humans in an Elderly Institution.Sensors (Basel, Switzerland) · 2023Article
- An EEG-based asynchronous MI-BCI system to reduce false positives with a small number of channels for neurorehabilitation: A pilot study.Frontiers in neurorobotics · 2022Article
- EEG-based emotion recognition using 4D convolutional recurrent neural network.Cognitive neurodynamics · 2020Article
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
Recent publications in the Electroencephalogram (EEG)-based brain-computer interface field suggest that this technology could be ready to go outside the research labs and enter the market as a new consumer product. This assumption is supported by the recent advantages obtained in terms of front-end graphical user interfaces, back-end classification algorithms, and technology improvement in terms of wearable devices and dry EEG sensors. This editorial paper aims at mentioning these aspects, starting from the review paper "Brain-Computer Interface Spellers: A Review" (Rezeika et al., 2018), published within the Brain Sciences journal, and citing other relevant review papers that discussed these points.
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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.