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 Interface (BCI) is an essential mechanism that interprets thehuman brain signal. It provides an assistive technology that enables personswith motor disabilities to communicate with the world and also empowers them tolead independent lives. The common BCI devices use Electroencephalography (EEG)electrical activity recorded from the scalp. EEG signals are noisy owing to thepresence of many artifacts, namely, eye blink, head movement, and jaw movement.Such artifacts corrupt the EEG signal and make EEG analysis challenging. Thisissue is addressed by locating the artifacts and excluding the EEG segment fromthe analysis, which could lead to a loss of useful information. However, wepropose a practical BCI that uses the artifacts which has a low signal to noiseratio.  The objective of our work is to classify different types of artifacts, namelyeye blink, head nod, head turn, and jaw movements in the EEG signal. Theoccurrence of the artifacts is first located in the EEG signal. The locatedartifacts are then classified using linear time and dynamic time warpingtechniques. The located artifacts can be used by a person with a motordisability to control a smartphone. A speech synthesis application that useseyeblinks in a single channel EEG system and jaw clinches in four channels EEGsystem are developed. Word prediction models are used for word completion, thusreducing the number of artifacts required.

Analysis of artifacts in EEG signals for building BCIs