Metadata
| Creators: | Klados, Manousos and Bamidis, Panagiotis |
|---|---|
| Divisions: | College of Engineering and Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical and Design |
| Data Publisher: | Mendeley |
| Date Made Available: | 16 October 2017 |
| Data Collection and Processing Methods: | This work presents a semi-simulated EEG dataset, where artifact-free EEG signals are manually contaminated with ocular artifacts following the model proposed by [1]. The significant part of this dataset is that it contains the pre-contamination EEG signals, so the brain signals underlying the EOG artifacts are known and thus the performance of every artifact rejection technique can be objectively assessed. The main differences of the proposed dataset compared to others (p.e. see [2,3]) is that it is focused only on EOG artifacts, using a realistic model for the contamination of artifact-free EEGs and not a random procedure. [1] T. Elbert, W. Lutzenberger, B. Rockstroh, N. Birbaumer, Removal of ocular artifacts from the EEG--a biophysical approach to the EOG., Electroencephalogr. Clin. Neurophysiol. 60 (1985) 455–63. http://www.ncbi.nlm.nih.gov/pubmed/2580697 (accessed April 10, 2013). [2] X. Yong, M. Fatourechi, R.K. Ward, G.E. Birch, Automatic artefact removal in a self-paced hybrid brain- computer interface system, J. Neuroeng. Rehabil. 9 (2012) 50. doi:10.1186/1743-0003-9-50. [3] A.K. Abdullah, C.Z. Zhang, A.A.A. Abdullah, S. Lian, Automatic Extraction System for Common Artifacts in EEG Signals Based on Evolutionary Stone’s BSS Algorithm, Math. Probl. Eng. 2014 (2014) 1–25. doi:10.1155/2014/324750. |
| Identification Number: | https://doi.org/10.17632/wb6yvr725d.3 |
Klados, Manousos and Bamidis, Panagiotis (2017) A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques. [Dataset] Mendeley. Available from: https://doi.org/10.17632/wb6yvr725d.3
Download
Full text not available from this repository.