Abstract
Most of the current Brain–Computer Interfaces (BCIs) application scenarios use electroencephalographic signals (EEG) containing the subject’s information. It means that if EEG were maliciously manipulated, the proper functioning of BCI frameworks could be at risk. Unfortunately, it happens in frameworks sensitive to noise-based cyberattacks, and more efforts are needed to measure the impact of these attacks. This work presents and analyzes the impact of four noise-based cyberattacks attempting to generate fake P300 waves in two different phases of a BCI framework. A set of experiments show that the greater the attacker’s knowledge regarding the P300 waves, processes, and data of the BCI framework, the higher the attack impact. In this sense, the attacker with less knowledge impacts 1% in the acquisition phase and 4% in the processing phase, while the attacker with the most knowledge impacts 22% and 74%, respectively.
Highlights
Brain–Computer Interfaces (BCIs) present a bidirectional communication channel between the brain and external devices
This paper proposes a study of the impact of cyberattacks focused on maliciously generating P300 in the EEG signal to determine the impact on BCI devices and, on end applications
These cyberattacks affect two different phases of the BCI cycle: (1) acquisition phase, where the noise is applied during the acquisition of the brain waves by the electrodes placed on the scalp, and (2) processing phase, in which the noise is applied once the data is in the BCI framework and has been processed by the third phase
Summary
Brain–Computer Interfaces (BCIs) present a bidirectional communication channel between the brain and external devices. Some authors detailed various cyberattacks targeting data confidentiality and user privacy [13, 17], while others focused on affecting the integrity of the EEG signal by attenuating evoked potentials [30]. There is a lack of literature on how the integrity of data managed by BCI frameworks can be compromised This weakness is complemented by a limited analysis of cyberattacks impact on the different phases of the BCI cycle. In this sense, this paper proposes a study of the impact of cyberattacks focused on maliciously generating P300 in the EEG signal to determine the impact on BCI devices and, on end applications.
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