Dictionary selection for compressed sensing of EEG signals using sparse binary matrix and spatiotemporal sparse Bayesian learning
- Submitting institution
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University of East London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 30
- Type
- D - Journal article
- DOI
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10.1088/2057-1976/abc133
- Title of journal
- Biomedical Physics & Engineering Express
- Article number
- -
- First page
- 065024
- Volume
- 6
- Issue
- 6
- ISSN
- 2057-1976
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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2 - Connected Devices and Systems
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A paradigm approach, employing compressed sensing (CS) to circumvent major constraints of high energy consumption and large volume of data in wireless sensor nodes for transmission of electroencephalogram (EEG) signals. Significant because it demonstrates for first time that it is essential to consider the Discrete Wavelet Transforms (DWT) key attributes of incoherence and vanishing moments together for EEG applications, rather than in isolation, this leading research is at forefront for employing CS and provides for a major influence on selection of a novel framework for EEG transmission with higher compression of data and lower errors in comparison to existing frameworks.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -