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-Computer Interfaces (BCI) have allowed for direct communication fromthe brain to external applications for the automatic detection of cognitiveprocesses such as error recognition. Error-related potentials (ErrPs) are aparticular brain signal elicited when one commits or observes an erroneousevent. However, due to the noisy properties of the brain and recording devices,ErrPs vary from instance to instance as they are combined with an assortment ofother brain signals, biological noise, and external noise, making theclassification of ErrPs a non-trivial problem. Recent works have revealedparticular cognitive processes such as awareness, embodiment, andpredictability that contribute to ErrP variations. In this paper, we explorethe performance of classifier transferability when trained on different ErrPvariation datasets generated by varying the levels of awareness and embodimentfor a given task. In particular, we look at transference between observationaland interactive ErrP categories when elicited by similar and differing tasks.Our empirical results provide an exploratory analysis into the ErrPtransferability problem from a data perspective.

Error-related Potential Variability: Exploring the Effects on Classification and Transferability