Performance Enhancement of EEG Signatures for Person Authentication Using CNN BiLSTM Method

• Published in: J.UCS (Annual print and CD-ROM archive ed.) Vol. 30; no. 12; pp. 1755 - 1779
• Main Authors: Mishra, Ashish Ranjan, Kumar, Rakesh, Saini, Rajkumar
• Format: Journal Article
• Language: English
• Published: Bristol Pensoft Publishers 01.01.2024; Graz University of Technology
• Subjects: Accuracy; Analysis; Artificial neural networks; Authentication; BiLSTM; Classification; CNN; Data acquisition; Data encryption chips; Deep Learning; EEG signals; Effectiveness; Electroencephalography; Feature extraction; Machine Learning; Maskininlärning; Methods; Neural networks; Noise reduction; Performance enhancement; Person Au; Person Authentication (PA); Signatures

Despite their vulnerability to competent forgers, signatures are one of the most widely used user verification methods. Recent research has revealed that EEG signals are harder to reproduce and give superior biometric information. This study aims to improve the effectiveness of person authentication...