ReviewSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2019
[A review of researches on electroencephalogram decoding algorithms in brain-computer interface].
Review in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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.
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Who cites it
5 citing papers in PubMed.
- Brain-Computer Interfaces: The Dawn of a New Era in Disease Treatment.Exploration (Beijing, China) · 2026Review
- A Methodology for Enhancing SSVEP Features Using Adaptive Filtering Based on the Spatial Distribution of EEG Signals.Micromachines · 2023Article
- [Review on identity feature extraction methods based on electroencephalogram signals].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Review
- [Progress of classification algorithms for motor imagery electroencephalogram signals].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- EEG Analysis with Wavelet Transform under Music Perception Stimulation.Journal of healthcare engineering · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-computer interface (BCI) provides a direct communicating and controlling approach between the brain and surrounding environment, which attracts a wide range of interest in the fields of brain science and artificial intelligence. It is a core to decode the electroencephalogram (EEG) feature in the BCI system. The decoding efficiency highly depends on the feature extraction and feature classification algorithms. In this paper, we first introduce the commonly-used EEG features in the BCI system. Then we introduce the basic classical algorithms and their advanced versions used in the BCI system. Finally, we present some new BCI algorithms proposed in recent years. We hope this paper can spark fresh thinking for the research and development of high-performance BCI system.
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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.