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

Biometric systems can identify individuals based on their unique characteristics. A new biometric based on hand synergies and their neural representations is proposed here. In this paper, ten subjects were asked to perform six hand grasps that are shared by most common activities of daily living. Their scalp electroencephalographic (EEG) signals were recorded using 32 scalp electrodes, of which 18 task-relevant electrodes were used in feature extraction. In our previous work, we found that hand kinematic synergies, or movement primitives, can be a potential biometric. In this paper, we combined the hand kinematic synergies and their neural representations to provide a unique signature for an individual as a biometric. Neural representations of hand synergies were encoded in spectral coherence of optimal EEG electrodes in the motor and parietal areas. An equal error rate of 7.5% was obtained at the system's best configuration. Also, it was observed that the best performance was obtained when movement specific EEG signals in gamma frequencies (30-50Hz) were used as features. The implications of these first results, improvements, and their applications in the near future are discussed.

Biometrics Based on Hand Synergies and Their Neural Representations