ReviewFrontiers in neuroscience2021
A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain-Computer Interface.
Review in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline 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
- Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.Neural regeneration research · 2026Article
- Bio-Inspired Gaze and Neural Command Fusion for Assistive Smartphone Interaction.Biomimetics (Basel, Switzerland) · 2026Article
- A lower-limb motor imagery BCI using virtual reality and novel calibration strategy in post-stroke patients.Medical & biological engineering & computing · 2026Article
- Current status and future prospects of brain-computer interfaces in the field of neurological disease rehabilitation.Frontiers in rehabilitation sciences · 2026Review
- Render EEG-Based Brain-Computer Interfaces Calibration-Free: Trade Space for Time in EEG Decoding.IEEE open journal of engineering in medicine and biology · 2026Article
- Brain-inspired signal processing for detecting stress during mental arithmetic tasks.Brain informatics · 2025Article
- Obstacle Feature Information-Based Motion Decision-Making Method for Obstacle-Crossing Motions in Lower Limb Exoskeleton Robots.Biomimetics (Basel, Switzerland) · 2025Article
- Brain-computer Interaction in the Smart Era.Current medical science · 2024Review
- Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs.Scientific reports · 2024Article
- Recruiting neural field theory for data augmentation in a motor imagery brain-computer interface.Frontiers in robotics and AI · 2024Article
- Identifying Thematics in a Brain-Computer Interface Research.Computational intelligence and neuroscience · 2023Review
- A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain-Computer Interface-Based Stroke Rehabilitation.Brain sciences · 2022Article
- Supervised and Semisupervised Manifold Embedded Knowledge Transfer in Motor Imagery-Based BCI.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In an electroencephalogram- (EEG-) based brain-computer interface (BCI), a subject can directly communicate with an electronic device using his EEG signals in a safe and convenient way. However, the sensitivity to noise/artifact and the non-stationarity of EEG signals result in high inter-subject/session variability. Therefore, each subject usually spends long and tedious calibration time in building a subject-specific classifier. To solve this problem, we review existing signal processing approaches, including transfer learning (TL), semi-supervised learning (SSL), and a combination of TL and SSL. Cross-subject TL can transfer amounts of labeled samples from different source subjects for the target subject. Moreover, Cross-session/task/device TL can reduce the calibration time of the subject for the target session, task, or device by importing the labeled samples from the source sessions, tasks, or devices. SSL simultaneously utilizes the labeled and unlabeled samples from the target subject. The combination of TL and SSL can take advantage of each other. For each kind of signal processing approaches, we introduce their concepts and representative methods. The experimental results show that TL, SSL, and their combination can obtain good classification performance by effectively utilizing the samples available. In the end, we draw a conclusion and point to research directions in the future.
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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.