Hybrid Brain/Blink Computer Interface toward a Personal Identification Number Application

Abstract

In this study, we propose a hybrid brain/blink computer interface based on a single-channel electroencephalography (EEG) amplifier. Eyelid closing and hard blink were selected as two possible inputs for control of the interface. A 2-min calibration was required before starting to use the interface. An algorithm for feature extraction and classification was developed for EEG signals from eyelid closing, hard blink, and resting. To evaluate the performance of the interface, we incorporated it into a personal identification number (PIN) application, in both visual and auditory modes. Experiments with 5 healthy participants revealed that the PIN application based on the interface achieved a mean accuracy of 97.40%. In conclusion, we expect that our investigation will contribute to hybrid brain-computer interface research and technologies in the near future.