| Title |
Neurofeedback training with a motor imagery-based BCI: neurocognitive improvements and EEG changes in the elderly
|
|---|---|
| Published in |
Medical & Biological Engineering & Computing, February 2016
|
| DOI | 10.1007/s11517-016-1454-4 |
| Pubmed ID | |
| Authors |
Javier Gomez-Pilar, Rebeca Corralejo, Luis F. Nicolas-Alonso, Daniel Álvarez, Roberto Hornero |
| 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. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Egypt | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 190 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 1% |
| United Kingdom | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 186 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 36 | 19% |
| Student > Ph. D. Student | 26 | 14% |
| Student > Bachelor | 25 | 13% |
| Researcher | 18 | 9% |
| Student > Postgraduate | 7 | 4% |
| Other | 24 | 13% |
| Unknown | 54 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 36 | 19% |
| Engineering | 22 | 12% |
| Psychology | 18 | 9% |
| Computer Science | 13 | 7% |
| Medicine and Dentistry | 11 | 6% |
| Other | 29 | 15% |
| Unknown | 61 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 July 2019.
All research outputs
#15,739,529
of 25,373,627 outputs
Outputs from Medical & Biological Engineering & Computing
#1,629
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Outputs of similar age
#163,278
of 313,053 outputs
Outputs of similar age from Medical & Biological Engineering & Computing
#7
of 23 outputs
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