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Abstrakty
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The aim of this study was to improve the robust weighted averaging based on criterion function minimization and assess its effectiveness for extracting event-related brain potentials (ERP) from electroencephalographic (EEG) recordings. The areas of improvement include significantly lower averaging error (45% lower RMSE and 37% lower maximum difference than for original implementation) and increased robustness to local minima, strong outliers and corrupted epochs common to real-life EEG signals, especially from low-cost devices. Our proposed procedure was tested on two datasets, one artificially generated for purposes of this study (including different noise sources) and one real-life dataset collected with Emotiv EPOCþ. The lower error results mainly from more effective rejection (lowering the weights) of corrupted epochs by integrating the correlation-based weighting. The advantages of our method over pure correlation-based weighting are lower RMSE (up to two times) and robustness to the algorithm initialization and strong outliers. The performance of the methods was measured using bootstrap testing to avoid dependency of results on data. It shows that our improvements lead to significantly lower error, especially when the EEG signal is not filtered. The values of the parameters were adjusted for EEG signals but they can easily be incorporated in other repetitive electrophysiological measurement techniques.
Twórcy
  • Institute of Informatics, Silesian University of Technology Gliwice, Poland
  • Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
autor
  • Institute of Electronics, Silesian University of Technology Gliwice, Poland
Bibliografia
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  • [3] Congedo M, Silva FHLd. Event-related potentials: general aspects of methodology and quantification. Niedermeyer's electroencephalography basic principles, clinical applications, and related fields. Oxford, UK: Oxford University Press; 2017, https://oxfordmedicine.com/view/10.1093/med/ 9780190228484.001.0001/med-9780190228484-chapter-39.
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  • [6] Kotowski K, Stapor K, Leski J, Kotas M. Validation of emotiv EPOCþ for extracting ERP correlates of emotional face processing. Biocybern Biomed Eng 2018;38(4):773–81. http://dx.doi.org/10.1016/j.bbe.2018.06.006, http://www.sciencedirect.com/science/article/pii/ S0208521618301748.
  • [7] Da Pelo P, De Tommaso M, Monaco A, Stramaglia S, Bellotti R, Tangaro S. Trial latencies estimation of event-related potentials in EEG by means of genetic algorithms. J Neural Eng 2018;15(2):026016. http://dx.doi.org/10.1088/1741-2552/aa9b97.
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  • [16] Makeig S, Bell AJ, Jung T-P, Sejnowski TJ. Independent component analysis of electroencephalographic data. In: Touretzky DS, Mozer MC, Hasselmo ME, editors. Advances in neural information processing systems, vol. 8. MIT Press; 1996. p. 145–51, http://papers.nips.cc/paper/1091-independent-component-analysis- of-electroencephalographic-data.pdf.
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  • [19] Parks NA, Gannon MA, Long SM, Young ME. Bootstrap signal-to-noise confidence intervals: an objective method for subject exclusion and quality control in ERP studies. Front Hum Neurosci 2016;10:50. http://dx.doi.org/10.3389/fnhum.2016.00050.
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  • [21] Congedo M. The analysis of event-related potentials. In: Im C-H, editor. Computational EEG analysis: methods and applications. Singapore: Springer Singapore; 2018. p. 55–82. http://dx.doi.org/10.1007/978-981-13-0908-3_4.
  • [22] Giroldini W, Pederzoli L, Bilucaglia M, Melloni S, Tressoldi P. A new method to detect event-related potentials based on Pearson's correlation. EURASIP J Bioinform Syst Biol 2016;2016(December (1)). http://dx.doi.org/10.1186/s13637-016-0043-z.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-18dbebf4-65d5-428a-9a3b-08ae44c3b57a
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