Evidence of Task-Independent Person-Specific Signatures in EEG using Subspace Techniques

Electroencephalography (EEG) signals are promising as alternatives to other biometrics owing to their protection against spoofing. Previous studies have focused on capturing individual variability by analyzing task/condition-specific EEG. This work attempts to model biometric signatures independent of task/condition by normalizing the associated variance. Toward this goal, the paper extends ideas from subspace-based text-independent speaker recognition and proposes novel modifications for modeling multi-channel EEG data. The proposed techniques assume that biometric information is present in the entire EEG signal and accumulate statistics across time in a high dimensional space. These high dimensional statistics are then projected to a lower dimensional space where the biometric information is preserved. The lower dimensional embeddings obtained using the proposed approach are shown to be task-independent. The best subspace system identifies individuals with accuracies of 86.4% and 35.9% on datasets with 30 and 920 subjects, respectively, using just nine EEG channels. The paper also provides insights into the subspace model's scalability to unseen tasks and individuals during training and the number of channels needed for subspace modeling.Comment: \copyright 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

EEG Authentication System Using Fuzzy Vault Scheme

Authentication is the process of recognizing a user’s identity by determining claimed user identity by checking user-provided evidence, combining cryptographic with biometric can solve many of security issues, including authentication. Our goal is to try to combine cryptography and biometrics to achieve authentication using fuzzy vault scheme. Electroencephalography (EEG) signals will be used as they are unique and also difficult to expose and copy; also they are difficult to be hack, using nine healthy persons’ EEGs from the BCI Competition and extracting power features from signals spectrum of beta and alpha band of EEG signal, the extracted features are from three channels (C3, Cz, and C4), then support vector Machine (SVM) is used for classification. In this chapter, two tasks (left hand and right hand) are used from a four tasks in the dataset, and the system achieves 96.98% validation accuracy, using 10-fold cross-validation on the training set and the model is saved, after extract features, these features will used to be evaluated on a polynomial generated from the secret key using reed Solomon code and chaff points generated using tent map are added to hide the data, which create the final result that is the vault, for decoding the system using Lagrange interpolation for polynomial reconstruction and returning the key