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

Recent studies have shown that the underlying neural mechanisms of humanspeech comprehension can be analyzed using a match-mismatch classification ofthe speech stimulus and the neural response. However, such studies have beenconducted for fixed-duration segments without accounting for the discreteprocessing of speech in the brain. In this work, we establish that wordboundary information plays a significant role in sentence processing byrelating EEG to its speech input. We process the speech and the EEG signalsusing a network of convolution layers. Then, a word boundary-based averagepooling is performed on the representations, and the inter-word context isincorporated using a recurrent layer. The experiments show that the modelingaccuracy can be significantly improved (match-mismatch classification accuracy)to 93% on a publicly available speech-EEG data set, while previous effortsachieved an accuracy of 65-75% for this task.

Enhancing the EEG Speech Match Mismatch Tasks With Word Boundaries