ArticleEntropy (Basel, Switzerland)2021
Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Brain computer interfaces for cognitive enhancement in older people - challenges and applications: a systematic review.BMC geriatrics · 2025Pooled it
- Effectiveness of Electroencephalography Neurofeedback for Improving Working Memory and Episodic Memory in the Elderly: A Meta-Analysis.Medicina (Kaunas, Lithuania) · 2024Pooled it
- Motor imagery-based neurofeedback in older adults: neural signatures and feasibility in a randomized controlled trial targeting age-related cognitive decline.Journal of neuroengineering and rehabilitation · 2026Trial
- Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study.PloS one · 2023Trial
- Takens-Based Kernel Transfer Entropy Connectivity Network for Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- Biofeedback for Motor and Cognitive Rehabilitation in Parkinson's Disease: A Comprehensive Review of Non-Invasive Interventions.Brain sciences · 2025Review
- The Application of Entropy in Motor Imagery Paradigms of Brain-Computer Interfaces.Brain sciences · 2025Review
- Improved motor imagery training for subject's self-modulation in EEG-based brain-computer interface.Frontiers in human neuroscience · 2024Article
- KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.