ReviewFrontiers in human neuroscience2021
A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain-Computer Interfaces.
Review in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Dataset Fragmentation, Cognitive Variability, and Reproducibility Challenges in EEG-Based Brain-Computer Interfaces: A PRISMA-Based Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions.Frontiers in human neuroscience · 2024Pooled it
- Knowledge mapping and research trends of brain-computer interface technology in rehabilitation: a bibliometric analysis.Frontiers in human neuroscience · 2024Pooled it
- Dual-Stream Fusion of Eye-Tracking and ECG Signals for Fatigue Detection in Remote Tower Air Traffic Controllers.Bioengineering (Basel, Switzerland) · 2026Article
- Rotation-based metric on the Riemannian manifold of SPD matrices with applications to source data selection for brain-computer interface transfer learning.Frontiers in human neuroscience · 2026Article
- Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection.Scientific reports · 2025Article
- Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.Sensors (Basel, Switzerland) · 2025Review
- Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning.Frontiers in neuroinformatics · 2025Article
- Brain-computer Interaction in the Smart Era.Current medical science · 2024Review
- A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning.Frontiers in human neuroscience · 2024Article
- A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.International journal of telemedicine and applications · 2023Review
- Automatic sleep staging of single-channel EEG based on domain adversarial neural networks and domain self-attention.Frontiers in neuroscience · 2023Article
- Semi-supervised generative and discriminative adversarial learning for motor imagery-based brain-computer interface.Scientific reports · 2022Article
- 2020 International brain-computer interface competition: A review.Frontiers in human neuroscience · 2022Review
- Comparing Methods of Feature Extraction of Brain Activities for Octave Illusion Classification Using Machine Learning.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has had a theoretical/practical impact on BCI research because of its use in learning representations of complex patterns inherent in EEG. Moreover, algorithmic advances in DL facilitate short/zero-calibration in BCI, thereby suppressing the data acquisition phase. Those advancements include data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the so-called data insufficiency problem in other datasets. In this study, we review DL-based short/zero-calibration methods for BCI. Further, we elaborate methodological/algorithmic trends, highlight intriguing approaches in the literature, and discuss directions for further research. In particular, we search for
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.