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A novel approach for designing authentication system using a picture based P300 speller

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Abstract

Due to great advances in the field of information technology, the need for a more reliable authentication system has been growing rapidly for protecting the individual or organizational assets from online frauds. In the past, many authentication techniques have been proposed like password and tokens but these techniques suffer from many shortcomings such as offline attacks (guessing) and theft. To overcome these shortcomings, in this paper brain signal based authentication system is proposed. A Brain–Computer Interface (BCI) is a tool that provides direct human–computer interaction by analyzing brain signals. In this study, a person authentication approach that can effectively recognize users by generating unique brain signal features in response to pictures of different objects is presented. This study focuses on a P300 BCI for authentication system design. Also, three classifiers were tested: Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor, and Quadratic Support Vector Machine. The results showed that the proposed visual stimuli with pictures as selection attributes obtained significantly higher classification accuracies (97%) and information transfer rates (37.14 bits/min) as compared to the conventional paradigm. The best performance was observed with the QDA as compare to other classifiers. This method is advantageous for developing brain signal based authentication application as it cannot be forged (like Shoulder surfing) and can still be used for disabled users with a brain in good running condition. The results show that reduced matrix size and modified visual stimulus typically affects the accuracy and communication speed of P300 BCI performance.

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Abbreviations

BCI:

Brain–computer interface

SSVEP:

Steady-state visual evoked potential

ERD, EEG:

Electroencephalogram

FAR:

False rejection rate

FRR:

False acceptance rate

ITR:

Information transfer rate

ERP:

Event-related potential

QDA:

Quadratic discriminant analysis

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Acknowledgements

The authors would like to thank the subjects for participating in this experiment.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. ICE Department, Dr. B. R. Ambedkar NIT Jalandhar, GT Road Bye-Pass, Jalandhar, Punjab, 144011, India

    Nikhil Rathi, Rajesh Singla & Sheela Tiwari

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  1. Nikhil Rathi
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  2. Rajesh Singla
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  3. Sheela Tiwari
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Correspondence to Nikhil Rathi.

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Experiments on human subjects had conducted with the ethical approval of the ethics committee of the institute.

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Rathi, N., Singla, R. & Tiwari, S. A novel approach for designing authentication system using a picture based P300 speller. Cogn Neurodyn 15, 805–824 (2021). https://doi.org/10.1007/s11571-021-09664-3

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