Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms

TL;DR

This paper introduces a multimodal biometric system combining EEG and keystroke dynamics, achieving high accuracy and robustness against attacks, outperforming individual modalities through machine learning classifiers.

Contribution

The study presents a novel multimodal biometric system integrating EEG and keystroke data, with a new dataset and improved classification accuracy over single modalities.

Findings

Achieved 99.80% accuracy with XGBoost classifier.

Combined modalities outperform individual ones by around 5%.

Binary template matching yields 93.64% accuracy, 6 times faster.

Abstract

With the rapid advancement of technology, different biometric user authentication, and identification systems are emerging. Traditional biometric systems like face, fingerprint, and iris recognition, keystroke dynamics, etc. are prone to cyber-attacks and suffer from different disadvantages. Electroencephalography (EEG) based authentication has shown promise in overcoming these limitations. However, EEG-based authentication is less accurate due to signal variability at different psychological and physiological conditions. On the other hand, keystroke dynamics-based identification offers high accuracy but suffers from different spoofing attacks. To overcome these challenges, we propose a novel multimodal biometric system combining EEG and keystroke dynamics. Firstly, a dataset was created by acquiring both keystroke dynamics and EEG signals from 10 users with 500 trials per user at 10 different sessions. Different statistical, time, and frequency domain features were extracted and ranked from the EEG signals and key features were extracted from the keystroke dynamics. Different classifiers were trained, validated, and tested for both individual and combined modalities for two different classification strategies - personalized and generalized. Results show that very high accuracy can be achieved both in generalized and personalized cases for the combination of EEG and keystroke dynamics. The identification and authentication accuracies were found to be 99.80% and 99.68% for Extreme Gradient Boosting (XGBoost) and Random Forest classifiers, respectively which outperform the individual modalities with a significant margin (around 5 percent). We also developed a binary template matching-based algorithm, which gives 93.64% accuracy 6X faster. The proposed method is secured and reliable for any kind of biometric authentication.