Browsing by Author "Jyoti Singh Singh Kirar"

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A Dual Adaptation Approach for EEG-Based Biometric Authentication Using the Ensemble of Riemannian Geometry and NSGA-II
(Springer Science and Business Media Deutschland GmbH, 2025) Aashish Khilnani; Jyoti Singh Singh Kirar; Ganga Ram Gautam
Recently, it has been discovered that EEG signals have enormous potential to be used as biometric authentication. Although, its practical implementation is limited due to the intricate and dynamic nature of EEG signals. To overcome these challenges, we need to simplify the analysis and preserve the spatial attributes of the EEG signals. In this work, a methodology using an ensemble of Riemannian geometry and a genetic algorithm for EEG-based biometric authentication is devised. Here, the symmetric positive definite covariance matrices of the EEG signals are calculated and classified using the Minimum distance to the Riemannian Mean (MDRM) and the Tangent space LDA (TSLDA) classifier. Furthermore, NSGA-II is used to optimize the number of channels and to reduce the computational complexity. This study achieved an accuracy of 99.9% on average with all the datasets used. Multiple publicly available datasets are used to compare the proposed approach with existing methods. Results obtained show the efficacy of the proposed method. Friedman’s statistical test also supports the statistical significance difference between the proposed and existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Enhancing Adversarial Attack Detection in EEG Signals With Covariance Entropy: A Novel Framework for BCI Security
(Institute of Electrical and Electronics Engineers Inc., 2025) Aashish Khilnani; Jyoti Singh Singh Kirar; Ganga Ram Gautam
Brain-computer interfaces (BCIs) facilitate direct brain-to-external device connection by using machine learning to interpret EEG data. However, these systems are vulnerable to adversarial attacks that can lead to faulty outputs and potentially severe consequences. This work presents a new method, Covariance Entropy (CovEn), for measuring an EEG signal’s uncertainty. It provides a more elaborative analysis of signal complexity compared to other entropy measures since it accounts for inter-channel variance and explicit correlation in EEG data. In our research, we used a variety of noisy and adversarial attacks to show that CovEn is a valuable tool for detecting signal contamination. This work introduces the advancement of reliable and safe neurotechnologies by highlighting the potential of CovEn as a protective mechanism against adversarial threats in multivariate signals. © 1994-2012 IEEE.