Biometrics Based on Hand Synergies and Their Neural Representations

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. Th...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2017-01, Vol.5, p.13422-13429
Main Authors: Patel, Vrajeshri, Burns, Martin, Chandramouli, Rajarathnam, Vinjamuri, Ramana
Format: Article
Language:English
Subjects:
Biometrics
Coherence
Electrodes
Electroencephalography
electroencephalography (EEG)
Feature extraction
hand kinematics
hand synergies
Iris recognition
Kinematics
movement primitives
neural representations
Representations
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2718003