Neurokey: Towards a new paradigm of cancelable biometrics-based key generation using electroencephalograms

Abstract

Background: Brain waves (Electroencephalograms, EEG) can provide conscious, continuous human authentication for the proposed system. The advantage of brainwave biometry is that it is nearly impossible to forge or duplicate as the neuronal activity of each person is unique even when they think about the same thing. Aim:We propose exploiting the brain as a biometric physical unclonable function (PUF). A user's EEG signals can be used to generate a unique and repeatable key that is resistant to cryptanalysis and eavesdropping, even against an adversary who obtains all the information regarding the system. Another objective is to implement a simplistic approach of cancelable biometrics by altering one's thoughts.Method: Features for the first step, Subject Authentication, are obtained from each task using the energy bands obtained from Discrete Fourier Transform and Discrete Wavelet Transform.The second step constituting the Neurokey generation involves feature selection using normalized thresholds and segmentation window protocol. Results:We applied our methods to two datasets, the first based on five mental activities by seven subjects (325 samples) and the second based on three visually evoked tasks by 120 subjects (10,861 samples). These datasets were used to analyze the key generation process because they varied in the nature of data acquisition, environment, and activities. We determined the feasibility of our system using a smaller dataset first. We obtained a mean subject classification of 98.46% and 91.05% for Dataset I and Dataset II respectively. After an appropriate choice of features, the mean half total error rate for generating Neurokeys was 3.05%

Brainwaves for User Verification using Two Separate Sets of Features based on DCT and Wavelet

“…The Receiver Operating Characteristic (ROC) Curve illustrates the performance of verification system by plotting the False Rejected rate (FRR) which is given by (19) and measures the proportion of incorrectly rejected genuine patterns, against the false Accepted rate (FAR) which is given by (18) and measures the proportion of incorrectly accepted imposter patterns, at various threshold settings to check the intersection point between FRR and FAR in which the Half Total Error rate (HTER) is calculated using (20) to evaluate the performance of the system [8], [24]:…”

“…Bajwa and Dantu [8] proposed the use of EEG signals for both authentication and cryptographic key generation. They used Fast Fourier Transform (FFT) and then Daubechies wavelet (db8) to extract features by calculating statistical information on the wavelet sub bands, in this paper DFT is proposed as a separate extraction method by calculating the energy averages of DFT's power spectra as well as wavelet Daubechies (db4) is proposed as a separate method by calculating the statistical moments to all sub bands.…”

Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification

“…The biometric features were represented by the spectral powers, and an accuracy of 91% was reported. 36 The Physionet Motor Movement/Imagery EEG data set (EEG-MMI), 37 including recordings made from 109 subjects via a 64-electrode EEG system, has been used in a number of the recent works in biometric recognition and identification. The recordings were made from the subjects during the rest periods with eyes open or eyes closed.…”

Multibiometric Fusion Authentication in Wireless Multimedia Environment Using Dynamic Bayesian Method

“…There has been a great deal of research on the application based on single biometric identification, especially in the fields of biological key [7][8][9], cloud computing data security [10][11][12][13], blockchain [14], privacy preserving [15][16][17][18], and biological template protection [19][20][21]. However, there are still not so many studies on multibiometrics.…”