User Identification of Keystroke Biometric Patterns with the Cognitive RAM Weightless Neural Net

Lai, Weng Kin; Tan, Beng Ghee; Soo, Ming Siong; Khan, Imran

doi:10.1007/978-3-319-32213-1_1

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most common properties used in current identity recognition systems are mainly based on human biological characteristics, such as fingerprints, face recognition (both optical and infrared), iris scanning [ 26 ], DNA [ 27 ], keystroke entry patterns [ 28 ] and even gait [ 29 ]. However, they still have limited capability to deal with forgery.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalogram-Based Subject Matching Learning (ESML): A Deep Learning Framework on Electroencephalogram-Based Biometrics and Task Identification

Xu,

Zhou,

Qin

et al. 2023

Behavioral Sciences

View full text Add to dashboard Cite

An EEG signal (Electroencephalogram) is a bioelectric phenomenon reflecting human brain activities. In this paper, we propose a novel deep learning framework ESML (EEG-based Subject Matching Learning) using raw EEG signals to learn latent representations for EEG-based user identification and tack classification. ESML consists of two parts: one is the ESML1 model via an LSTM-based method for EEG-user linking, and one is the ESML2 model via a CNN-based method for EEG-task linking. The new model ESML is simple, but effective and efficient. It does not require any restrictions for EEG data collection on motions and thinking for users, and it does not need any EEG preprocessing operations, such as EEG denoising and feature extraction. The experiments were conducted on three public datasets and the results show that ESML performs the best and achieves significant performance improvement when compared to baseline methods (i.e., SVM, LDA, NN, DTS, Bayesian, AdaBoost and MLP). The ESML1 model provided the best precision at 96% with 109 users and the ESML2 model achieved 99% precision at 3-Class task classification. These experimental results provide direct evidence that EEG signals can be used for user identification and task classification.

show abstract