ReviewFrontiers in aging neuroscience2016
Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment.
Review in Frontiers in aging neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain-Computer Interfaces Systems for Upper and Lower Limb Rehabilitation: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- A Comprehensive Survey of Brain-Computer Interface Technology in Health care: Research Perspectives.Journal of medical signals and sensors · 2025Review
- Elevating Chest X-ray Image Super-Resolution with Residual Network Enhancement.Journal of imaging · 2024Article
- Motor imagery classification using sparse representations: an exploratory study.Scientific reports · 2023Article
- Article
- Identifying Thematics in a Brain-Computer Interface Research.Computational intelligence and neuroscience · 2023Review
- Hybrid classification model for eye state detection using electroencephalogram signals.Cognitive neurodynamics · 2022Article
- EEG Signal Classification Using Manifold Learning and Matrix-Variate Gaussian Model.Computational intelligence and neuroscience · 2021Article
- Trends in Compressive Sensing for EEG Signal Processing Applications.Sensors (Basel, Switzerland) · 2020Review
- A toolbox for brain network construction and classification (BrainNetClass).Human brain mapping · 2020Article
- Article
- Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification.Computational intelligence and neuroscience · 2018Article
- Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain.Frontiers in neuroscience · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals.
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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.