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 feat...