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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Brain signals can be applied to human-computer interaction. By using the brain signals, we can detect the attention. Based on event-related potential P300 signals, the brain-machine interface enables the users to select the desired letters only by means of attention. Previous studies have reported the feasibility of the P300 signals with single subject to realize a novel information retrieval. In recent years, the collaborative EEG of the multiple subjects has been studied, with which the classification performance to detect attention was remarkably improved. In this paper, we propose the emotional face retrieval with the P300 signals of the multiple subjects. The number of subjects in the multiple subjects condition was 12. As a result, the F-measure value in the single subject condition was 0.618 ± 0.046 (standard deviation), and in the multiple subjects condition was 0.832. In conclusion, the classification performance of emotional face retrieval can be improved with collaborative P300 signals from multiple subjects. This technique might be applied to life log, computer supported cooperative work and neuromarketing in future.

Computer-Generated Emotional Face Retrieval with P300 Signals of Multiple Subjects