English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Brain-machine interfaces, also known as Brain-Computer Interfaces (BCI), are devices that connect the brain to a computer. In the last decade it is been used to investigate the restore of sensorimotor function in patients with cortical damage. Some evidence supports that the results obtained in the research using BCIs in the treatment of these patients are directly dependent on plasticity mechanisms very similar to those observed in learning and memory tasks. Recent studies have been making use of Artificial Neural Networks (ANN) to classify signal patterns throughout neuronal circuits in order to command movements . In this review, we attempted to make a survey on the available data in the literature on this topic in order to gather the most relevant evidence of such mechanisms and the role of ANN on human brain neuronal input decoding compared to other data processing techniques.

Neuronal plasticity mechanisms induced by brain-machine interfaces: connecting brain to artificial neural network