EEG-based biometric identification using frequency-weighted power feature

• Published in: IET biometrics Vol. 9; no. 6; pp. 251 - 258
• Main Authors: Monsy, Jijomon Chettuthara, Vinod, Achutavarrier Prasad
• Format: Journal Article
• Language: English
• Published: Stevenage The Institution of Engineering and Technology 01.11.2020; John Wiley & Sons, Inc
• Subjects: Access control; Accuracy; art EEG features; Biometric identification; biometric identification technique; Biometrics; biometrics (access control); brain; brain activity; brainwave‐based biometric identification; Classification; correlation‐based classifier; corresponding power density value; Data acquisition; Data collection; EEG; Electrodes; Electroencephalography; eyes‐closed resting‐state; Frequencies; frequency‐weighted power feature; FWP; higher discrimination; medical signal processing; Methods; Neural networks; neurophysiology; nonstationary EEG signal; Nuclear power plants; Pattern recognition systems; person identification; Physiology; relevant subject‐specific information; Research Article; robust features; Signal processing; specific band; specific frequency band; sufficient discriminability; unique neuronal signature; Wavelet transforms; brainwave-based biometric identification; electroencephalography; correlation-based classifier; sufficient discriminability; specific frequency band; unique neuronal signature; nonstationary EEG signal; art EEG features; brain; biometrics (access control); biometric identification technique; specific band; medical signal processing; higher discrimination; relevant subject-specific information; person identification; eyes-closed resting-state; frequency-weighted power feature; robust features; neurophysiology; brain activity; corresponding power density value; FWP
• ISSN: 2047-4938; 2047-4946; 2047-4946
• DOI: 10.1049/iet-bmt.2019.0158

The brain activity of a person sensed using electroencephalograph (EEG) has a unique neuronal signature consisting of relevant subject-specific information, which can be used for biometric identification. The main challenge in brainwave-based biometric identification is the extraction of robust features from non-stationary EEG signal that provide sufficient discriminability to differentiate between individuals. In this study, the authors propose an EEG-based biometric identification technique using a novel feature called frequency-weighted power (FWP), which offers higher discrimination in the person identification compared to the state of the art EEG features. FWP is an equivalent representation of the power of a specific frequency band, obtained by multiplying the specific frequency with its corresponding power density value and summing up it over the specific band. The efficacy of the proposed method is validated using resting-state EEG from online PhysioNet database as well as using resting-state EEG data acquired from 16 subjects in the laboratory during the experiment. Using a correlation-based classifier, the proposed method achieves an equal error rate (EER) of 0.0039 from eyes-closed resting-state EEG signals using 20 electrodes, which is nearly one-fifth of the EER obtained by the best method reported in the literature for the comparable number of electrodes.