Dataset for: Decoding Motor Plans Using a Closed-Loop Ultrasonic Brain-Machine Interface

This dataset accompanies "Decoding Motor Plans Using a Closed-Loop Ultrasonic Brain-Machine Interface". It includes the 2 Hz real-time data (.mat files), metadata about each session (`project_record.json`), and description of the contents of each .mat file (`DescriptionOfVariables.pdf`).Abstract of "Decoding Motor Plans Using a Closed-Loop Ultrasonic Brain-Machine Interface" Brain-machine interfaces (BMIs) enable people living with chronic paralysis to control computers, robots, and more with nothing but thought. Existing BMIs have tradeoffs across invasiveness, performance, spatial coverage, and spatiotemporal resolution. Functional ultrasound (fUS) neuroimaging is an emerging technology that balances these attributes and may complement existing BMI recording technologies. In this study, we use fUS to demonstrate a successful implementation of a closed-loop ultrasonic BMI. We streamed fUS data from the posterior parietal cortex of two rhesus macaque monkeys while they performed eye and hand movements. After training, the monkeys controlled up to eight movement directions using the BMI. We also developed a method for pretraining the BMI using data from previous sessions. This enabled immediate control on subsequent days, even those that occurred months apart, without requiring extensive recalibration. These findings establish feasibility of ultrasonic BMIs, paving the way for a new class of less invasive (epidural) interfaces that generalize across extended time periods and promise to restore function to people with neurological impairments.