ArticleComputational and mathematical methods in medicine2020
Task Transfer Learning for EEG Classification in Motor Imagery-Based BCI System.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Decoding Upper-Limb Motor Imagery from EEG Signals: A Systematic Review of Methods and Applications.Annals of biomedical engineering · 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
- Using machine learning to simultaneously quantify multiple cognitive components of episodic memory.Nature communications · 2025Article
- [Execution, assessment and improvement methods of motor imagery for brain-computer interface].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain-Computer Interface.Frontiers in neuroscience · 2021Review
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The motor-imagery brain-computer interface system (MI-BCI) has a board prospect for development. However, long calibration time and lack of enough MI commands limit its use in practice. In order to enlarge the command set, we add the combinations of traditional MI commands as new commands into the command set. We also design an algorithm based on transfer learning so as to decrease the calibration time for collecting EEG signal and training model. We create feature extractor based on data from traditional commands and transfer patterns through the data from new commands. Through the comparison of the average accuracy between our algorithm and traditional algorithms and the visualization of spatial patterns in our algorithm, we find that the accuracy of our algorithm is much higher than traditional algorithms, especially as for the low-quality datasets. Besides, the visualization of spatial patterns is meaningful. The algorithm based on transfer learning takes the advantage of the information from source data. We enlarge the command set while shortening the calibration time, which is of significant importance to the MI-BCI application.
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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.