Medical & Biological Engineering & Computing

, Volume 54, Issue 11, pp 1655–1666 | Cite as

Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly

  • Javier Gomez-Pilar
  • Rebeca Corralejo
  • Luis F. Nicolas-Alonso
  • Daniel Álvarez
  • Roberto Hornero
Original Article

Abstract

Neurofeedback training (NFT) has shown to be promising and useful to rehabilitate cognitive functions. Recently, brain–computer interfaces (BCIs) were used to restore brain plasticity by inducing brain activity with an NFT. In our study, we hypothesized that an NFT with a motor imagery-based BCI (MI-BCI) could enhance cognitive functions related to aging effects. To assess the effectiveness of our MI-BCI application, 63 subjects (older than 60 years) were recruited. This novel application was used by 31 subjects (NFT group). Their Luria neuropsychological test scores were compared with the remaining 32 subjects, who did not perform NFT (control group). Electroencephalogram changes measured by relative power (RP) endorsed cognitive potential findings under study: visuospatial, oral language, memory, intellectual and attention functions. Three frequency bands were selected to assess cognitive changes: 12, 18, and 21 Hz (bandwidth 3 Hz). Significant increases (p < 0.01) in the RP of these frequency bands were found. Moreover, results from cognitive tests showed significant improvements (p < 0.01) in four cognitive functions after performing five NFT sessions: visuospatial, oral language, memory, and intellectual. This established evidence in the association between NFT performed by a MI-BCI and enhanced cognitive performance. Therefore, it could be a novel approach to help elderly people.

Keywords

Brain–computer interface (BCI) Neurofeedback training (NFT) Electroencephalogram (EEG) Luria adult neuropsychological diagnosis (Luria-AND) Elderly people 

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Copyright information

© International Federation for Medical and Biological Engineering 2016

Authors and Affiliations

  • Javier Gomez-Pilar
    • 1
  • Rebeca Corralejo
    • 1
  • Luis F. Nicolas-Alonso
    • 1
  • Daniel Álvarez
    • 1
  • Roberto Hornero
    • 1
  1. 1.Biomedical Engineering Group, E.T.S.I. de TelecomunicaciónUniversidad de ValladolidValladolidSpain

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