Collection Method: This work presents a semi-simulated EEG dataset, where artifact-free EEG signals are manually contaminated with ocular artifacts following the model proposed by . 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.  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).  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.  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.
|Date made available||17 Jul 2016|