RT info:eu-repo/semantics/article
T1 Neurofeedback training based on motor imagery strategies increases EEG complexity in elderly population
A1 Marcos Martínez, Diego
A1 Martínez Cagigal, Víctor
A1 Santamaría Vázquez, Eduardo
A1 Pérez Velasco, Sergio
A1 Hornero Sánchez, Roberto
K1 Biofeedback training
K1 Entrenamiento de biorretroalimentación
K1 Entropía
K1 Brain-computer interfaces
K1 Older people - Care
K1 Personas de edad - Salud mental
K1 Cognición
K1 Neuropsychology
K1 Neuropsicología
K1 Neurofeedback training
K1 Motor imagery
K1 3201.07 Geriatría
K1 3205.07 Neurología
AB 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.
PB MDPI
SN 1099-4300
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/59100
UL https://uvadoc.uva.es/handle/10324/59100
LA eng
NO Entropy, 2021, Vol. 23, Nº. 12, 1574
NO Producción Científica
DS UVaDOC
RD 23-nov-2024