Evaluation of filters over different stimulation models in evoked potentials

• Autorzy: Güven Aysegül , Batbat Turgay

Identyfikatory

DOI

10.1016/j.bbe.2018.08.007

• Języki publikacji: EN

Abstrakty

EN

Filtering is a key process which removes unwanted parts of signals. During signal recording, various forms of noises distort data. Physiological signals are highly noise sensitive and to evaluate them powerful filtering approaches must be applied. The aim of this study is to compare modern filtering approaches on scalp signals. Brain activities were generally examined by brain signals like EEG and evoked potentials (EP). In this study, data were recorded from university students whose age between 18 and 25 years with visual and auditory stimuli. Discrete wavelet transforms, singular spectrum analysis, empirical mode decomposition and discrete Fourier transform based filters were used and compared with raw data on classification performance. Higuchi fractal dimension and entropy features were extracted from EEG; P300 features were extracted from EP signals. Classification was applied with support vector machines. All filtered data gave better scores than raw data. Empirical mode decomposition (EMD) and Fourier-based filter yielded lower results than the discrete wavelet-based filter. Singular spectrum analysis gave the best result at 84.32%. The current study suggests that singular spectrum analysis removes noise from sensitive physiological signals, and EMD requires new mode selection procedures before resynthesizing.

Słowa kluczowe

EN

electroencephalography signal; visual evoked potentials; auditory evoked potentials; support vector machine

PL

elektroencefalografia; wzrokowe potencjały wywołane; słuchowe potencjały wywołane; maszyna wektorów wsparcia

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 2
• Strony: 339--349
• Opis fizyczny: Bibliogr. 38 poz., rys., tab., wykr.

Twórcy

autor

Güven Aysegül

• Faculty of Engineering, Erciyes University, Kayseri, Turkey

autor

Batbat Turgay

• Faculty of Engineering, Erciyes University, Kayseri, Turkey

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).