Identifying optimal channels and features for multi-participant motor imagery experiments across a participant’s multi-day multi-class EEG data-Reference-Cited by-同舟云学术

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Identifying optimal channels and features for multi-participant motor imagery experiments across a participant’s multi-day multi-class EEG data

Published:2023-03-27 Issue: Volume: Page:
ISSN:1871-4080
Container-title:Cognitive Neurodynamics
language:en
Short-container-title:Cogn Neurodyn

Author:

Kaya Esra^ORCID,Saritas Ismail^ORCID

Funder

Selçuk University Research Foundation

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience

Link

https://link.springer.com/content/pdf/10.1007/s11571-023-09957-9.pdf

Reference40 articles.

1. Abu-Rmileh A, Zakkay E, Shmuelof L, Shriki O (2019) Co-adaptive training improves efficacy of a multi-day EEG-based motor imagery BCI training. Front Hum Neurosci 13:362

2. Afrakhteh S, Mosavi MR (2020) Applying an efficient evolutionary algorithm for EEG signal feature selection and classification in decision-based systems. Energy efficiency of medical devices and healthcare applications. Elsevier, pp 25–52

3. Alyasseri ZAA, Khader AT, Al-Betar MA, Abasi AK, Makhadmeh SN (2019) EEG signals denoising using optimal wavelet transform hybridized with efficient metaheuristic methods. IEEE Access 8:10584–10605. https://doi.org/10.1109/ACCESS.2019.2962658

4. Amo C, De Santiago L, Barea R, López-Dorado A, Boquete L (2017) Analysis of gamma-band activity from human EEG using empirical mode decomposition. Sensors 17(5):989. https://doi.org/10.3390/s17050989

5. Ashour AS, Guo Y, Hawas AR, Xu G (2018) Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images. Health Inform Sci Syst 6(1):1–10. https://doi.org/10.1007/s13755-018-0059-8

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