A Cybersecure P300-Based Brain-to-Computer Interface against Noise-Based and Fake P300 Cyberattacks

Giovanni Mezzina,Daniela De Venuto,Valerio F Annese

doi:10.3390/s21248280

Giovanni Mezzina, Daniela De Venuto + Show 1 more

Open Access

https://doi.org/10.3390/s21248280

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Abstract

In a progressively interconnected world where the Internet of Things (IoT), ubiquitous computing, and artificial intelligence are leading to groundbreaking technology, cybersecurity remains an underdeveloped aspect. This is particularly alarming for brain-to-computer interfaces (BCIs), where hackers can threaten the user’s physical and psychological safety. In fact, standard algorithms currently employed in BCI systems are inadequate to deal with cyberattacks. In this paper, we propose a solution to improve the cybersecurity of BCI systems. As a case study, we focus on P300-based BCI systems using support vector machine (SVM) algorithms and EEG data. First, we verified that SVM algorithms are incapable of identifying hacking by simulating a set of cyberattacks using fake P300 signals and noise-based attacks. This was achieved by comparing the performance of several models when validated using real and hacked P300 datasets. Then, we implemented our solution to improve the cybersecurity of the system. The proposed solution is based on an EEG channel mixing approach to identify anomalies in the transmission channel due to hacking. Our study demonstrates that the proposed architecture can successfully identify 99.996% of simulated cyberattacks, implementing a dedicated counteraction that preserves most of BCI functions.

Highlights

As a proof of concept, we focus on a P300-based interface trained using support vector machine (SVM) algorithms, but the proposed method is versatile and can be applied to other classifiers for brain-to-computer interfaces (BCIs)
Our study focuses on SVMs, which are widely used for BCI implementation
According to the inhibition procedure, the brain hacking recognizer (BHR) forces the BCI to label the trial as system, it to readily detect and inhibit cyberattack

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. A brain-to-computer interface (BCI) is a direct communication channel between the user’s neural activity and electronic devices [1]. BCIs are based on the recognition of a neural pattern during a specific mental task [1]. The neural activity can be recorded with both invasive and non-invasive equipment. Electroencephalography (EEG) is the most commonly used non-invasive technique in BCI systems [2]. Machine learning and classification algorithms are used to analyze the neural signals, recognize the target neural pattern, and interpret the user’s intention

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