A Novel Technique for Selecting EMG-Contaminated EEG Channels in Self-Paced Brain-Computer Interface Task Onset
- Submitting institution
-
The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1263
- Type
- D - Journal article
- DOI
-
10.1109/TNSRE.2018.2847316
- Title of journal
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Article number
- -
- First page
- 1353
- Volume
- 26
- Issue
- 7
- ISSN
- 1534-4320
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- 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
-
1
- Research group(s)
-
B - Brain Computer Interfaces and Neural Engineering (BCI-NE)
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper addresses the decades long problem of involuntarily, facial myoelectric activity (fEMG) leading to false positives, especially when investigating the onset of cognitive states in electroencephalograph (EEG) analysis. Previous techniques (e.g., blind source separation) have been used for reducing this problem, but they lead to loss of important EEG information. Significantly this paper introduces a method based on simultaneous recording of EEG and fEMG and frequency-band based analysis that produces significant fEMG removal whilst reducing EEG loss, significantly outperforming previous approaches, thus potentially benefitting all areas involving EEG analysis. It is the foundation to further work.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
