Neuronal Jamming Cyberattack over Invasive BCI Affecting the Resolution of Tasks Requiring Visual Capabilities
Sergio L\'opez Bernal, Alberto Huertas Celdr\'an, Gregorio Mart\'inez, P\'erez

TL;DR
This paper introduces a novel neuronal jamming cyberattack on invasive brain-computer interfaces that disrupts neural activity and impairs task performance, highlighting cybersecurity vulnerabilities and potential health risks.
Contribution
It presents the design and implementation of Neuronal Jamming (JAM), a new cyberattack method that effectively disrupts neural spikes in simulated and biological networks, surpassing existing attacks.
Findings
JAM significantly reduces neuronal spike rates.
JAM impairs the mouse’s ability to complete the maze.
JAM has a higher impact than FLO in disrupting neural activity.
Abstract
Invasive Brain-Computer Interfaces (BCI) are extensively used in medical application scenarios to record, stimulate, or inhibit neural activity with different purposes. An example is the stimulation of some brain areas to reduce the effects generated by Parkinson's disease. Despite the advances in recent years, cybersecurity on BCI is an open challenge since attackers can exploit the vulnerabilities of invasive BCIs to induce malicious stimulation or treatment disruption, affecting neuronal activity. In this work, we design and implement a novel neuronal cyberattack, called Neuronal Jamming (JAM), which prevents neurons from producing spikes. To implement and measure the JAM impact, and due to the lack of realistic neuronal topologies in mammalians, we have defined a use case with a Convolutional Neural Network (CNN) trained to allow a mouse to exit a particular maze. The resulting…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces · Neuroscience and Neural Engineering · Advanced Memory and Neural Computing
