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

Abstract

Electroencephalography (EEG) based biometric systems are gaining attention for their anti-spoofing capability but lack accuracy due to signal variability at different psychological and physiological conditions. On the other hand, keystroke dynamics-based systems achieve very high accuracy but have low anti-spoofing capability. To address these issues, a novel multimodal biometric system combining EEG and keystroke dynamics is proposed in this paper. A dataset was created by acquiring both keystroke dynamics and EEG signals simultaneously from 10 users. Each user participated in 500 trials at 10 different sessions (days) to replicate real-life signal variability. A machine learning classification pipeline is developed using multi-domain feature extraction (time, frequency, time-frequency), feature selection (Gini impurity), classifier design, and score level fusion. Different classifiers were trained, validated, and tested for two different classification experimentspersonalized and generalized. For identification and authentication, 99.9% and 99.6% accuracies are achieved, respectively for Random Forest classifier using 5 fold cross-validation. These results outperform the individual modalities with a significant margin (~5%). We also developed a binary template matching-based algorithm, which gives 6X faster system with 93.64% accuracy. The proposed method can be considered secure and reliable for any kind of biometric identification and authentication.

Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification Using Nerve Conduction Studies

“…This study focuses on the performance analysis of different conventional ML algorithm based DSPN severity classifiers using NCS variables. Here, we trained 8 different algorithms: ensemble classifier (EC), random forest (RF), K-nearest neighbour (KNN), Decision Trees (DT), support vector machine (SVM), naive Bayes (NB), logistic regression (LR), and discriminant analysis classifier (DAC) [ 3 , 36 , 38 , 47 , 48 ]. Fitcauto function from MATLAB 2020b (The MathWorks, Inc., Natick, Massachusetts, United States) was used for training and hyperparameters' tuning of the models.…”

“…In the present paper, we have investigated the performance of eight different conventional ML algorithms such as ensemble classifier (EC), random forest (RF), K-nearest neighbour (KNN), decision trees (DT), support vector machine (SVM), Naive Bayes (NB), logistic regression (LR), and discriminant analysis classifier (DAC) for severity classification of DSPN using NCS. The choice of algorithms studied in this study was based on the commonly used conventional ML algorithms in disease classification problems based on literature [ 3 , 36 , 38 , 47 , 48 ]. Ten nerve attributes have been considered for DSPN severity grading in this study.…”

Machine Learning-Based Epileptic Seizure Detection Methods Using Wavelet and EMD-Based Decomposition Techniques: A Review

“…Basic statistics, for example, the mean, variance, standard deviation, and kurtosis [24] can be used in the extraction phase, and their formulae are provided below. Note that the standard deviation, σ, is just the square root of the variance, σ 2 .…”

Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis

“…The usages have potential applications in the personal healthcare domain. Recently, due to the inherent anti-spoofing capability of EEG signals, the implementation of biometric systems using EEG is being studied and already has shown promising outcomes [17].…”