Biometrics Based on Hand Synergies and their Neural Representations
Abstract-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 preliminary study 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 the hand kinematic synergies, or movement primitives, can be a potential biometric. In the current work, 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.
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