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

Understanding how brain functions has been an intriguing topic for years.With the recent progress on collecting massive data and developing advancedtechnology, people have become interested in addressing the challenge ofdecoding brain wave data into meaningful mind states, with many machinelearning models and algorithms being revisited and developed, especially theones that handle time series data because of the nature of brain waves.However, many of these time series models, like HMM with hidden state indiscrete space or State Space Model with hidden state in continuous space, onlywork with one source of data and cannot handle different sources of informationsimultaneously. In this paper, we propose an extension of State Space Model towork with different sources of information together with its learning andinference algorithms. We apply this model to decode the mind state of studentsduring lectures based on their brain waves and reach a significant betterresults compared to traditional methods.

SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data