Evaluating algorithms of removing EOG artifacts with experimental data in brain computer interface
- 309 Downloads
In this paper, the authors investigate and compare two types of algorithms for removing EOG artifacts in EEG measurements. First, the sources that introduce EOG artifacts are explained and the authors briefly review the existing algorithms for EOG artifacts removal in EEG signals. Two selected algorithms—recursive least square and blind source separation are discussed in details. These two algorithms were chosen either for their good performance or for their low computational charge to be used in ambulatory monitoring devices. In the third part, the experimental protocol to collect EOG and EEG measurements are described. In the fourth part, the authors summarize the results after applying two algorithms to the collected real EEG data. From these results, conclusions are drawn on the performances of these selected algorithms. Several possible improvements or challenges are proposed in the discussion section.
KeywordsEOG artifacts Artifacts removal Recursive least square Blind source separation Brain computer interface
The authors would like to thank the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (No. TP2015029) for financial support.
- 1.Atwood, H.L., MacKay, W.A.:. Essentials of neurophysiology, B.C. Decker, Hamilton, Canada, 1989, pp. 200–205 (1993)Google Scholar
- 2.Tyner, F.S., Knott, J.R.: Fundamentals of EEG Technology: Basic Concepts and Methods, vol. 1. Raven Press, New York (1989)Google Scholar
- 3.Niedermeyer, E., Lopes da Silva, F.H.: Electroencephalog-Raphy: Basic Principles, Clinical Applications and Related Fields, 3rd edn, pp. 351–354. Lippincott, Williams & Wilkins, Philadelphia (1993)Google Scholar
- 11.Woestengurg, J.C., Verbaten, M.N., Slangen, J.L.: The removal of the eye movement artifact from the EEG by regression analysis in the frequency domain. Biol. Physiol. 16, 127–147 (1983)Google Scholar
- 19.Zhou, W., Gotman, J.: Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA. In: Proceedings of 26th Annual International Conference IEEE Engineering in Medicine and Biology Society(EMBS04), San Francisco, CA, Sep. 15, pp. 392–395 (2004)Google Scholar
- 20.Nguyen, H.-A.T., Musson, J., Li, J., McKenzie, F., Zhang, G., Zu, R., Richey, C., Schell, T.: EEG artifact removal using a wavelet neural network. In: Proceedings of Mod Sim World (2010)Google Scholar
- 21.Jung, T.-P., Humphries, C., Lee, T.-W., Makeig, S., McKeown, M.J., Iragui, V., Sejnowski, T.J.: Extended ICA removes artifacts from electroencephalographic recordings. Adv. Neural Inf. Process. Syst. 10, 894–900 (1998)Google Scholar
- 27.Rowland, V.: Cortical steady potential (direct current potential) in reinforcement and learning. Prog. Physiol. Psychol. 2, 1–77 (1968)Google Scholar