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

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.

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Acknowledgments

This research was supported in part by the Projects TEC2014-53196-R of ‘Ministerio de Economía y Competitividad’ and FEDER, the ‘Proyecto Cero’ 2011 on Ageing from Fundación General CSIC, Obra Social La Caixa and CSIC, and the Project VA059U13 of “Consejería de Educación”. Finally, J. Gomez-Pilar was in receipt of a PIF-UVA Grant from University of Valladolid.

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Correspondence to Javier Gomez-Pilar.

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Gomez-Pilar, J., Corralejo, R., Nicolas-Alonso, L.F. et al. Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly. Med Biol Eng Comput 54, 1655–1666 (2016) doi:10.1007/s11517-016-1454-4

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Keywords

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