Augmented Complex Common Spatial Patterns for Classification of Noncircular EEG From Motor Imagery Tasks
- Submitting institution
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Royal Holloway and Bedford New College
- Unit of assessment
- 12 - Engineering
- Output identifier
- 31369214
- Type
- D - Journal article
- DOI
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10.1109/TNSRE.2013.2294903
- Title of journal
- IEEE Transactions on Neural Systems and Rehabilitation
- Article number
- -
- First page
- 1
- Volume
- 22
- Issue
- 1
- ISSN
- 1534-4320
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2014
- 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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2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first work on how complex-valued statistics can be exploited to analyse and detect brain activities in the right and left hemispheres, when a subject “imagines” moving their hand, although the subject does not actually do the hand movement. This research is a reference for many works in brain motor activities (e.g. Tomida 2014, Chowdhury 2018). Beyond this problem, this work has been fundamental in facilitating other multivariate analyses such as singular spectrum analysis, empirical mode decomposition and considered in other neuroscience applications, e.g. sleep analysis, EEG enhancement, and detection of event related potentials that reflect decision making.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
