EEG-Based Biometrics Utilizing Image Recognition for Patient Identification

Abstract

Biometric identification has been applied widely for security purpose in many different fields by using fingerprints, face detection, or voice waves. In medical fields, using patient wristband or patient card for identification may cause the medical records to be mistaken. To overcome these limitations, in this paper, a new method is presented by using electroencephalogram (EEG) signals to classify the patient's identity, hence preventing treating the wrong patient. The system utilizes various hardware and software such as OpenBCI Cyton, EEGlab, MATLAB, and bandpass filter. The main purpose of this research is highlighting the recognition of each EEG signal pattern from each person by capturing the signals from watching a series of images that trigger attentions and memories.