5,467
Views
12
CrossRef citations to date
0
Altmetric
Research Article

A study on quality assessment of the surface EEG signal based on fuzzy comprehensive evaluation method

, , , , &

References

  • Minguillon J, Lopez-Gordo MA, Pelayo F. Trends in EEG-BCI for daily-life: requirements for artifact removal. Biomed Signal Process Control. 2017;31:407–418.
  • Kim K, Lim S H, Lee J, et al. Joint maximum likelihood time delay estimation of unknown event-related potential signals for EEG sensor signal quality enhancement. Sensors 2016;16(6):891.
  • Tautan AM, Serdijn W, Mihajlovic V, et al. Framework for evaluating EEG signal quality of dry electrode recordings. IEEE Biomedical Circuits and Systems Conference; Rotterdam: IEEE; 2013. p. 186–189.
  • Hu B, Peng H, Zhao QL, et al. Signal quality assessment model for wearable EEG sensor on prediction of mental stress. IEEE Trans Nanobiosci. 2015;14(5):553–561.
  • Viswam N, Roozbeh J. Characterizing contact impedance, signal quality and robustness as a function of the cardinality and arrangement of fingers on dry contact EEG electrodes. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; Chicago (IL): IEEE; 2014;3755–3758.
  • Tautan AM, Mihajlovic V, Chen YH, et al. Signal quality in dry electrode EEG and the relation to skin-electrode contact impedance magnitude. Proceedings of the 7th International Conference on Biomedical Electronics and Devices; Angers (Loire Valley, France); 2014. p. 12–22.
  • Fiedler P, Brodkorb S, Fonseca C, et al. Novel TiN-based dry EEG electrodes: influence of electrode shape and number on contact impedance and signal quality. In: Bamidis PD, Pallikarakis N, editors. XII Mediterranean Conference on Medical and Biological Engineering and Computing. Vol. 29. Berlin (Heidelberg): Springer; 2010; p. 418–421.
  • Fiedler P, Fonseca C, Pedrosa P, et al. Novel flexible Dry multipin electrodes for EEG: signal quality and interfacial impedance of Ti and TiN coatings. Proc Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:547–550.
  • Nakamura M, Chen Q, Sugi T, et al. Technical quality evaluation of EEG recording based on electroencephalographers’ knowledge. Med Eng Phys. 2005;27(1):93–100.
  • Yang HJ, Guan CT, Ang KK, et al. Quality assessment of EEG Signals based on Statistics of Signal Fluctuations. IEEE International Conference on Systems, Man and Cybernetics; IEEE; 2014; p. 1853–1857.
  • Shady M, Sherif H, Saeid N, et al. Towards automated quality assessment measure for EEG signals. Neurocomputing 2017;237:281–290.
  • Gu WJ, Cao FL, Dong W. Analysis on the application of bio-nanotechnology in forests based on fuzzy comprehensive assessment. J Computat Theor Nanosci. 2016;13(7):4625–4628.