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Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population

1
Biomedical Engineering Group, E.T.S.I. Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
2
Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Carlos M. Travieso-González
Entropy 2021, 23(12), 1574; https://doi.org/10.3390/e23121574
Received: 21 September 2021 / Revised: 22 November 2021 / Accepted: 24 November 2021 / Published: 25 November 2021
(This article belongs to the Section Signal and Data Analysis)
Neurofeedback training (NFT) has shown promising results in recent years as a tool to address the effects of age-related cognitive decline in the elderly. Since previous studies have linked reduced complexity of electroencephalography (EEG) signal to the process of cognitive decline, we propose the use of non-linear methods to characterise changes in EEG complexity induced by NFT. In this study, we analyse the pre- and post-training EEG from 11 elderly subjects who performed an NFT based on motor imagery (MI–NFT). Spectral changes were studied using relative power (RP) from classical frequency bands (delta, theta, alpha, and beta), whilst multiscale entropy (MSE) was applied to assess EEG-induced complexity changes. Furthermore, we analysed the subject’s scores from Luria tests performed before and after MI–NFT. We found that MI–NFT induced a power shift towards rapid frequencies, as well as an increase of EEG complexity in all channels, except for C3. These improvements were most evident in frontal channels. Moreover, results from cognitive tests showed significant enhancement in intellectual and memory functions. Therefore, our findings suggest the usefulness of MI–NFT to improve cognitive functions in the elderly and encourage future studies to use MSE as a metric to characterise EEG changes induced by MI–NFT. View Full-Text
Keywords: neurofeedback training (NFT); motor imagery (MI); sample entropy; multiscale entropy (MSE); brain–computer interfaces (BCI); elderly people; age-relate cognitive decline; Luria adult neuropsychological diagnosis (Luria-AND) neurofeedback training (NFT); motor imagery (MI); sample entropy; multiscale entropy (MSE); brain–computer interfaces (BCI); elderly people; age-relate cognitive decline; Luria adult neuropsychological diagnosis (Luria-AND)
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MDPI and ACS Style

Marcos-Martínez, D.; Martínez-Cagigal, V.; Santamaría-Vázquez, E.; Pérez-Velasco, S.; Hornero, R. Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population. Entropy 2021, 23, 1574. https://doi.org/10.3390/e23121574

AMA Style

Marcos-Martínez D, Martínez-Cagigal V, Santamaría-Vázquez E, Pérez-Velasco S, Hornero R. Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population. Entropy. 2021; 23(12):1574. https://doi.org/10.3390/e23121574

Chicago/Turabian Style

Marcos-Martínez, Diego, Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, Sergio Pérez-Velasco, and Roberto Hornero. 2021. "Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population" Entropy 23, no. 12: 1574. https://doi.org/10.3390/e23121574

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