BRAIN SIGNATURES PERSPECTIVE FOR HIGH-SECURITY AUTHENTICATION

2020 ◽  
Vol 32 (04) ◽  
pp. 2050025
Author(s):  
Nikhil Rathi ◽  
Rajesh Singla ◽  
Sheela Tiwari
Keyword(s):  
Classification Accuracy ◽  
Information Transfer ◽  
Rejection Rate ◽  
Internet Banking ◽  
P300 Speller ◽  
Security Authentication ◽  
Information Transfer Rate ◽  
Shoulder Surfing ◽  
Authentication System ◽  
High Level

In the recent past, the web (internet) has emerged as the most interactive authentication system for all of us (i.e. Internet banking passwords, system or building access, and e-payment platforms, etc.) and as a result, traditional authentication systems (like passwords or token-based) are never again more secure i.e. they are vulnerable to attacks. As a result, the security of individual information and safe access to a system winds up prime necessities. Therefore, the EEG-based authentication system has recently become a reasonable key for high-level security. This study centers upon P300 evoked potential-based authentication system designing. In this paper, a new visual stimulus paradigm (i.e. [Formula: see text] P300 speller) using pictures of different objects as stimuli for a person authentication system is designed instead of the conventional character-based paradigm (i.e. [Formula: see text] speller) for increasing the classification accuracy and Information Transfer Rate (ITR). The trial begins by exhibiting a collection of pictures of various objects on four corners of the PC screen comprising of random object pictures (non-target) alongside password pictures (target) that trigger P300 reactions. The P300 reaction’s rightness then checks the identity of the subject concerning the focused pictures (Target). The proposed investigation model achieves higher classification accuracy of 96.78%, along with 0.03075 False Rejection Rate (FRR), 0.03297 False Acceptation Rate (FAR), and ITR of [Formula: see text]. This study has shown that P300-based authentication system has an advantage over conventional methods (Password, Token, etc.) as EEG-based systems cannot be mimicked or forged (like Shoulder surfing in case of password) and can still be used for disabled users with a brain in good running condition. The classification results revealed that the performance of the QDA classifier outperformed other classifiers based on accuracy and ITR.

scholarly journals A Spelling Paradigm With an Added Red Dot Improved the P300 Speller System Performance

2020 ◽  
Vol 14 ◽  
Author(s):  
Yan Wu ◽  
Weiwei Zhou ◽  
Zhaohua Lu ◽  
Qi Li
Keyword(s):  
Classification Accuracy ◽  
Information Transfer ◽  
Event Related Potential ◽  
Virtual Character ◽  
P300 Speller ◽  
Information Transfer Rate ◽  
Control Paradigm ◽  
Character Matrix ◽  
Green Circle ◽  
Visuospatial Information

The traditional P300 speller system uses the flashing row or column spelling paradigm. However, the classification accuracy and information transfer rate of the P300 speller are not adequate for real-world application. To improve the performance of the P300 speller, we devised a new spelling paradigm in which the flashing row or column of a virtual character matrix is covered by a translucent green circle with a red dot in either the upper or lower half (GC-RD spelling paradigm). We compared the event-related potential (ERP) waveforms with a control paradigm (GC spelling paradigm), in which the flashing row or column of a virtual character matrix was covered by a translucent green circle only. Our experimental results showed that the amplitude of P3a at the parietal area and P3b at the frontal–central–parietal areas evoked by the GC-RD paradigm were significantly greater than those induced by the GC paradigm. Higher classification accuracy and information transmission rates were also obtained in the GC-RD system. Our results indicated that the added red dots increased attention and visuospatial information, resulting in an amplitude increase in both P3a and P3b, thereby improving the performance of the P300 speller system.

scholarly journals Novel Biometric Fusion System using GA-PSO and ANN

2020 ◽  
Vol 9 (4) ◽  
pp. 3122-3129
Keyword(s):  
Rejection Rate ◽  
Fusion System ◽  
Scale Invariant ◽  
Fusion Strategy ◽  
Bee Colony ◽  
Face Features ◽  
Authentication System ◽  
Nature Inspired Algorithms ◽  
High Level ◽  
Biometric Fusion

Technology advancements have led to the emergence of biometrics as the most relevant future authentication technology. On practical grounds, unimodal biometric authentication systems have inevitable momentous limitations due to varied data quality and noise levels. The paper aims at investigating fusion of face and fingerprint biometric characteristics to achieve a high level personal authentication system. In the fusion strategy face features are extracted using Scale-Invariant Feature Transform (SIFT) algorithm and fingerprint features are extracted using minutiae feature extraction. These extracted features are optimized using nature inspired Genetic Algorithm (GA). The efficiency of the proposed fusion authentication system is enhanced by training and testing the data by applying Artificial Neural Network (ANN). The quality of the proposed design is evaluated against two nature inspired algorithms, namely, Particle Swarm Optimization (PSO)and Artificial Bee Colony (ABC) in terms of False Acceptance Rate (FAR), False Rejection Rate (FRR) and recognition accuracy. Simulation results over a range of image sample from 10 to 100 images have shown that the proposed biometric fusion strategy resulted in FARof 2.89, FAR 0.71and accuracy 97.72%.Experimental evaluation of the proposed system also outperformed the existing biometric fusion system.

scholarly journals Parallel Computing Sparse Wavelet Feature Extraction for P300 Speller BCI

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Zhihua Huang ◽  
Minghong Li ◽  
Yuanye Ma
Keyword(s):  
Feature Extraction ◽  
Parallel Computing ◽  
Information Transfer ◽  
Small Scale ◽  
Online Experiments ◽  
P300 Speller ◽  
Information Transfer Rate ◽  
Desktop Computers ◽  
The Difference ◽  
Computing Scheme

This work is intended to increase the classification accuracy of single EEG epoch, reduce the number of repeated stimuli, and improve the information transfer rate (ITR) of P300 Speller. Target EEG epochs and nontarget EEG ones are both mapped to a space by Wavelet. In this space, Fisher Criterion is used to measure the difference between target and nontarget ones. Only a few Daubechies wavelet bases corresponding to big differences are selected to construct a matrix, by which EEG epochs are transformed to feature vectors. To ensure the online experiments, the computation tasks are distributed to several computers that are managed and integrated by Storm so that they could be parallelly carried out. The proposed feature extraction was compared with the typical methods by testing its performance of classifying single EEG epoch and detecting characters. Our method achieved higher accuracies of classification and detection. The ITRs also reflected the superiority of our method. The parallel computing scheme of our method was deployed on a small scale Storm cluster containing three desktop computers. The average feedback time for one round of EEG epochs was 1.57 ms. The proposed method can improve the performance of P300 Speller BCI. Its parallel computing scheme is able to support fast feedback required by online experiments. The number of repeated stimuli can be significantly reduced by our method. The parallel computing scheme not only supports our wavelet feature extraction but also provides a framework for other algorithms developed for P300 Speller.

scholarly journals Wireless Stimulus-on-Device Design for Novel P300 Hybrid Brain-Computer Interface Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Chung-Hsien Kuo ◽  
Hung-Hsuan Chen ◽  
Hung-Chyun Chou ◽  
Ping-Nan Chen ◽  
Yu-Cheng Kuo
Keyword(s):  
Independent Living ◽  
Information Transfer ◽  
Spinal Cord Injuries ◽  
Support Vector ◽  
Svm Classifier ◽  
Bee Colony ◽  
Information Transfer Rate ◽  
Control Scheme ◽  
Interval Type ◽  
High Level

Improving the independent living ability of people who have suffered spinal cord injuries (SCIs) is essential for their quality of life. Brain-computer interfaces (BCIs) provide promising solutions for people with high-level SCIs. This paper proposes a novel and practical P300-based hybrid stimulus-on-device (SoD) BCI architecture for wireless networking applications. Instead of a stimulus-on-panel architecture (SoP), the proposed SoD architecture provides an intuitive control scheme. However, because P300 recognitions rely on the synchronization between stimuli and response potentials, the variation of latency between target stimuli and elicited P300 is a concern when applying a P300-based BCI to wireless applications. In addition, the subject-dependent variation of elicited P300 affects the performance of the BCI. Thus, an adaptive model that determines an appropriate interval for P300 feature extraction was proposed in this paper. Hence, this paper employed the artificial bee colony- (ABC-) based interval type-2 fuzzy logic system (IT2FLS) to deal with the variation of latency between target stimuli and elicited P300 so that the proposed P300-based SoD approach would be feasible. Furthermore, the target and nontarget stimuli were identified in terms of a support vector machine (SVM) classifier. Experimental results showed that, from five subjects, the performance of classification and information transfer rate were improved after calibrations (86.00% and 24.2 bits/ min before calibrations; 90.25% and 27.9 bits/ min after calibrations).

scholarly journals Introducing Rhythmic Sinusoidal Amplitude-Modulated Auditory Stimuli with Multiple Message Frequency Coding for Fatigue Reduction in Normal Subjects: An EEG Study

2019 ◽  
Author(s):  
Elham Shamsi ◽  
Zahra Shirzhiyan ◽  
Ahmadreza Keihani ◽  
Morteza Farahi ◽  
Amin Mahnam ◽  
...  
Keyword(s):  
Signal Processing ◽  
Correlation Coefficient ◽  
Classification Accuracy ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Normal Subjects ◽  
Auditory Stimuli ◽  
Frequency Coding ◽  
Information Transfer Rate ◽  
Auditory Bci

AbstractMany of the brain-computer interface (BCI) systems depend on the user’s voluntary eye movements. However, voluntary eye movement is impaired in people with some neurological disorders. Since their auditory system is intact, auditory paradigms are getting more patronage from researchers. However, lack of appropriate signal-to-noise ratio in auditory BCI necessitates using long signal processing windows to achieve acceptable classification accuracy at the expense of losing information transfer rate. Because users eagerly listen to their interesting stimuli, the corresponding classification accuracy can be enhanced without lengthening of the signal processing windows. In this study, six sinusoidal amplitude-modulated auditory stimuli with multiple message frequency coding have been proposed to evaluate two hypotheses: 1) these novel stimuli provide high classification accuracies (greater than 70%), 2) the novel rhythmic stimuli set reduces the subjects’ fatigue compared to its simple counterpart. We recorded EEG from nineteen normal subjects (twelve female). Five-fold cross-validated naïve Bayes classifier classified EEG signals with respect to power spectral density at message frequencies, Pearson’s correlation coefficient between the responses and stimuli envelopes, canonical correlation coefficient between the responses and stimuli envelopes. Our results show that each stimuli set elicited highly discriminative responses according to all the features. Moreover, compared to the simple stimuli set, listening to the rhythmic stimuli set caused significantly lower subjects’ fatigue. Thus, it is worthwhile to test these novel stimuli in a BCI experiment to enhance the number of commands and reduce the subjects’ fatigue.Significance StatementAuditory BCI users eagerly listen to the stimuli they are interested in. Thus, response classification accuracy may be enhanced without the need for trial lengthening. Since humans enjoy listening to rhythmic sounds, this study was carried out for introducing novel rhythmic sinusoidal amplitude-modulated auditory stimuli with multiple message frequency coding. Our results show that each stimuli set evoked reliably discriminative responses according to all the features, and rhythmic stimuli set caused significantly lower fatigue in subjects. Thus, it is worthwhile to test these novel stimuli in a BCI study to increase the number of commands (by NN permutations of just N message frequencies) and reduce the subjects’ fatigue.

A Research Combining Spatial Filter with Autoregressive Model for EEG Feature Extraction

2010 ◽  
Vol 20-23 ◽  
pp. 605-611 ◽  
Author(s):  
Lin Liu ◽  
Qing Guo Wei
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Autoregressive Model ◽  
Information Transfer ◽  
Decisive Role ◽  
Spatial Filter ◽  
Fisher Discriminant Analysis ◽  
Left Hand ◽  
Fisher Discriminant ◽  
Information Transfer Rate

In a noninvasive brain-computer interface (BCI), EEG feature extraction is a key part for improving classification accuracy and resulting information transfer rate, and it has a crucial and decisive role. In this paper, three different methods were proposed that combine spatial filtering with autoregressive model for EEG feature extraction. Six subjects participated in the BCI experiment during which they were asked to imagine movements of left hand and right hand. Each subject carried out four sessions and each session contained 120 trials. EEG data recordings were used for off-line analysis and the 10 leads around C3 and C4 were chosen for feature extraction. Autoregressive model coefficients and the parameters derived from other three methods were proposed as classification features. Fisher discriminant analysis (FDA) was used as linear classifier. The results show that classification accuracy rates obtained from the three proposed methods are far higher than those acquired from autoregressive model coefficients. At the same time the classification results of each subject are very stable, proving the effectiveness of these novel feature methods.

A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface

2018 ◽  
Vol 28 (10) ◽  
pp. 1850034 ◽  
Author(s):  
Wei Li ◽  
Mengfan Li ◽  
Huihui Zhou ◽  
Genshe Chen ◽  
Jing Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Rate ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Generation Rate ◽  
Information Transfer Rate ◽  
Command Generation

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

German Seismic and Infrasound Networks Contributing to the European Integrated Data Archive (EIDA)

2021 ◽  
Vol 92 (3) ◽  
pp. 1854-1875 ◽  
Author(s):  
Klaus Stammler ◽  
Monika Bischoff ◽  
Andrea Brüstle ◽  
Lars Ceranna ◽  
Stefanie Donner ◽  
...  
Keyword(s):  
Information Transfer ◽  
Data Exchange ◽  
Seismic Hazard Assessment ◽  
Data Archive ◽  
Waveform Data ◽  
Level Data ◽  
Data Products ◽  
Global Seismicity ◽  
High Level ◽  
Test Ban

Abstract Germany has a long history in seismic instrumentation. The installation of the first station sites was initiated in those regions with seismic activity. Later on, with an increasing need for seismic hazard assessment, seismological state services were established over the course of several decades, using heterogeneous technology. In parallel, scientific research and international cooperation projects triggered the establishment of institutional and nationwide networks and arrays also focusing on topics other than monitoring local or regional areas, such as recording global seismicity or verification of the compliance with the Comprehensive Nuclear-Test-Ban Treaty. At each of the observatories and data centers, an extensive analysis of the recordings is performed providing high-level data products, for example, earthquake catalogs, as a base for supporting state or federal authorities, to inform the public on topics related to seismology, and for information transfer to international institutions. These data products are usually also accessible at websites of the responsible organizations. The establishment of the European Integrated Data Archive (EIDA) led to a consolidation of existing waveform data exchange mechanisms and their definition as standards in Europe, along with a harmonization of the applied data quality assurance procedures. In Germany, the German Regional Seismic Network as national backbone network and the state networks of Saxony, Saxony-Anhalt, Thuringia, and Bavaria spearheaded the national contributions to EIDA. The benefits of EIDA are attracting additional state and university networks, which are about to join the EIDA community now.

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