My name is Emma Szczesniak and I'm a graduate student in the Department of Biomedical Engineering.
And today I'm presenting research on the cybersecurity of motor-based neural interfaces.
Which are devices that connect technology with the nervous system to enhance motor function.
These include myoelectric and brain-computer interfaces, which use signals from the muscles or brain to control external devices.
While originally developed for rehabilitation and prosthetics. These devices have since expanded into gaming and entertainment.
Reaching a much broader user base. While neural interface research focuses on improving device accuracy and usability.
Security concerns are often overlooked. Just like your phone or computer can be hacked.
So can a neural interface. Imagine someone being able to access or manipulate your neural data.
Or even control the device itself. This isn't just a tech issue.
It's a safety issue. Our research focuses on the signal transmission stage of neural interfaces.
Which is when neural data is sent from the user's body to the computer.
We aim to identify frequency hopping patterns to understand how attackers might detect or intercept data.
And analyze frequency difference in wrist motor tasks To determine if attackers could exploit them for data manipulation.
We began by building a myoelectric computer interface system to classify wrist movements using electromyography.
For wireless communication, we used Gazelle, a transmission protocol similar to Bluetooth Low Energy.
And common in neural interface systems. Allowing us to study vulnerabilities within a realistic communication framework.
To test for vulnerabilities, we used a HackRF1 device, which lets us listen in on frequency transmission.
Similar to a Bluetooth sniffer. We targeted the 2.4 to 2.5 gigahertz range, which is the widely used frequency band for wireless transmission.
Next, we looked at frequency characteristics of the motor task EMG data.
If an attacker knows what a normal flexion or extension signal looks like, they could target specific frequencies associated with a motor task To spoof the system by sending false but believable signals.
We study the mean, median, and peak frequencies of reflection and extension.
Chosen for their susceptibility to manipulation and thus higher exploitation potential.
Moving on to our results, we first analyzed the frequency transmission data using the HACRF1.
In the spectrograms here, you can see signal activity over time in the 2.4 to 2.5 gigahertz range.
The thresholded view highlights clear points of frequency hopping, where the signal jumps between channels.
By zooming into smaller frequency bands, we were able to identify specific frequencies involved in this hopping.
Understanding these patterns allows for eavesdropping or interception attacks. That opened the door for more malicious actions such as spoofing.
Next, we analyzed the frequency characteristics of EMG data from wrist movements.
As shown in the box plots, the differences in frequency metrics between the two motor tasks We're distinct enough for a targeted attack to be possible.
We also found that electrode placement had a minimal impact on frequency metrics.
Suggesting that these characteristics are generalizable across a range of neural interface systems.
Regardless of their electrode setup. So why is this important?
Imagine if you were lifting a heavy object or descending stairs.
When an attacker hijacks your device. They can then initiate unintended movements and cause physical harm to you or people in your vicinity.
Losing control of your device can also cause fear. Frustration, and a loss of trust.
Which may discourage continued device use. For people who rely on these devices for daily tasks.
This could seriously impact their quality of life. In summary, we found that vulnerabilities in signal transmission, such as detectable frequency hopping patterns and distinct frequency characteristics in motor tasks.
Can expose a myoelectric computer interface systems to potential attacks. As these systems become more heavily integrated into daily life.
Prioritizing security is essential to protect user safety and device functionality.
Looking ahead, our next step is to run a man-in-the-middle attack To test whether we can actually intercept and send false data to the system.
Since this study only identified potential weak points. Validating them in practice is essential to understand the real-world risks and guide the development of effective security measures.
Thank you for listening to my virtual presentation, and I welcome you to reach out to me with questions.