Cybersecurity in Brain-Computer Interfaces: RFID-based design-theoretical framework

Ajrawi, Shams Al; Rao, Ramesh R.; Sarkar, Mitali

doi:10.1016/j.imu.2020.100489

Cited by 15 publications

(11 citation statements)

References 22 publications

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“…The objective of the paper in [115] is to ensure security in a network supporting BCI applications to identify brain activities in a real-time mode. To achieve such goal, the authors of this paper proposed a Radio Frequency Identification (RFID)-based system made of semi-active RFID tags placed on the patient's scalp.…”

Section: Data Securitymentioning

confidence: 99%

“…Brain-computer interfacing is a real-time communication system that connects the brain and external devices. A BCI system can directly convert the information sent by the brain into commands that can drive external devices and can replace human limbs or phonation organs to achieve communication with the outside world and to control the external environment [115]. In other words, a BCI system can replace the normal peripheral nerve and muscle tissue to achieve communication between a human and a computer or between a human and the external environment [115].…”

Section: Progress On Applications Of Brain-computer Interfaces To Conmentioning

confidence: 99%

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An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

et al. 2021

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The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.

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Section: Data Securitymentioning

confidence: 99%

Section: Progress On Applications Of Brain-computer Interfaces To Conmentioning

confidence: 99%

An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

et al. 2021

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“…Several solutions, summarized in Table 1, have been proposed to improve the security of a neural interface [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. The proposed methods typically use one or more solutions to supervise the BCI session [17,18], authenticate the user [19][20][21][22][23], encrypt the data [24][25][26][27], and ultimately detect cyberattacks [28][29][30].…”

Section: Related Workmentioning

confidence: 99%

“…Authentication-based solutions establish a cybersecure communication channel between the user/sensors and the BCI framework. Authentication can involve the use of additional hardware, including user-specific radio frequency identification (RFID) technology [24], near-field or directional communication [28], and cameras for facial recognition [27]. Brainprint biometric authentication systems with no additional hardware using user-specific signatures in the EEG data have also been developed [20,21].…”

Section: Related Workmentioning

confidence: 99%

“…Navigation unit (camera, laser odometry) P300-based navigation system [17] Navigation unit (wheel encoders, proximity sensors, gyro sensor, and an RGBD sensor) P300-driven wheelchair [23] Authentication RFID technology Bidirectional BCI [24] Near-field communication User framework [28] Facial recognition IoT framework [27] Brainprint biometric authentication P300 speller [25] BCI framework [26] Encryption BCI anonymizer BCI Framework [29] Tensor-based data representation EEG data [30] Chaotic encryption EEG data [18] Randomization BCI Framework [19] Cyberattack identification software…”

Section: Supervisionmentioning

confidence: 99%

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

Mezzina

Annese

Venuto

2021

Sensors

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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.

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Linking Computers to the Brain: Overview of Cybersecurity Threats and Possible Solutions

Lahtinen

Costin

2023

Lecture Notes in Business Information Processing

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Cybersecurity in Brain-Computer Interfaces: RFID-based design-theoretical framework

Cited by 15 publications

References 22 publications

An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

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

Linking Computers to the Brain: Overview of Cybersecurity Threats and Possible Solutions

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