Non parametric directionality analysis - extension for removal of a single common predictor and application to time series
- Submitting institution
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University of York
- Unit of assessment
- 12 - Engineering
- Output identifier
- 55024260
- Type
- D - Journal article
- DOI
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10.1016/j.jneumeth.2016.05.008
- Title of journal
- Journal of Neuroscience Methods
- Article number
- -
- First page
- 87
- Volume
- 268
- Issue
- -
- ISSN
- 0165-0270
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2016
- 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
- Yes
- Number of additional authors
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3
- Research group(s)
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B - Intelligent Systems and Nano-Science
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The ability to establish cause and effect is critical to understanding information processing in the brain, this paper presents a novel non-parametric model-free approach. MATLAB software is freely available: http://www.neurospec.org/. This signal processing work has enabled epidemiological studies linking public health to air pollution in the United States (Contact: Senior partner, US based AI/ML analytics company). It led to a collaboration with the University of Oxford, National Hospital Queen Square and The Wellcome Centre for Human Neuroimaging on a project to quantify abnormal neural pathways in Parkinson’s Disease (https://doi.org/10.1152/jn.00629.2017).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -