Neurofeedback Training with a Motor Imagery-Based BCI Improves Neurocognitive Functions in Elderly People

Abstract

In recent years, brain-computer interfaces (BCIs) have become not only a tool to provide communication and control for people with disabilities, but also a way to rehabilitate some motor or cognitive functions. Brain plasticity can help restore normal brain functions by inducing brain activity. In fact, voluntary event-related desynchronization (ERD) in upper alpha and beta electroencephalogram (EEG) activity bands is associated with different neurocognitive functions. In this regard, neurofeedback training (NFT) has shown to be a suitable way to control one’s own brain activity. Furthermore, new evidence in recent studies showed NFT could lead to microstructural changes in white and grey matter. In our novel study, NFT qualities were applied to aging-related effects. We hypothesized that a NFT by means of motor imagery-based BCI (MI-BCI) could affect different cognitive functions in elderly people. To assess the effectiveness of this application, we studied 63 subjects, all above 60 years old. The subjects were divided into a control group (32 subjects) and a NFT group (31 subjects). To validate the effectiveness of the NFT using MI-BCI, variations in the scores of neuropsychological tests (Luria tests) were measured and analyzed. Results showed significant improvements (p < 0.05) in the NFT group, after only five NFT sessions, in four cognitive functions: visuospatial; oral language; memory; and intellectual. These results further support the association between NFT and the enhancement of cognitive performance. Findings showed the potential usefulness of NFT using MI-BCI. Therefore, this approach could lead to new means to help elderly people.