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

The use of time- and frequency-based features has proven effective in theprocess of classifying mental tasks in Brain Computer Interfaces (BCIs). Still,most of those methods provide little insight about the underlying brainactivity and functions. Thus, a better understanding of the mechanisms anddynamics of brain activity, is necessary in order to obtain useful andinformative features for BCIs. In the present study, the objective is toinvestigate the differences in functional connectivity of two motor imagerytasks, through a partial directed coherence (PDC) analysis, which is afrequency-domain metric that provides information about directionality in theinteraction between signals recorded at different channels. Four healthysubjects participated in this study, two mental tasks were evaluated:Imagination of the movement of the right hand or left hand. We carry out thedifferentiation of these tasks through two different approaches: on one hand,the traditional one based on spectral power; on the other hand, an approachbased on PDC. The results showed that EEG-based PDC analysis providesadditional information and it can potentially improve the feature selectionmainly in the beta frequency band.

Preliminary Assessment of hands motor imagery in theta- and beta-bands for Brain-Machine-Interfaces using functional connectivity analysis