  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.
