Meghna Menon

Xinyun Zou, Jorge Gamez, Meghna Menon et al.

Brain-computer interfaces (BCIs) read neural signals directly from the brain to infer motor planning and execution. However, the implementation of this technology has been largely limited to laboratory settings, with few real-world applications. We developed a BCI system to drive a vehicle in both simulated and real-world environments. We demonstrate that an individual with tetraplegia, implanted with intracortical BCI electrodes in the posterior parietal cortex (PPC) and the hand knob region of the motor cortex (MC), reacts at least as fast and precisely as motor intact participants. This BCI participant, living in California, could also remotely drive a Ford Mustang Mach-E vehicle in Michigan. Our teledriving tasks relied on cursor movement control for speed and steering in a closed urban test facility and through a predefined obstacle course. These two tasks serve as a proof-of-concept that takes into account the safety and feasibility of BCI-controlled driving. The final BCI system added click control for full-stop braking and thus enabled bimanual cursor-and-click control for simulated town driving with the same proficiency level as the motor intact control group through a virtual town with traffic. This first-of-its-kind implantable BCI application not only highlights the versatility and innovative potentials of BCIs but also illuminates the promising future for the development of life-changing solutions to improve independent mobility for those who suffer catastrophic neurological injury.

Mithun Goutham, Meghna Menon, Sarah Garrow et al.

The convex hull cheapest insertion heuristic produces good solutions to the Euclidean Traveling Salesperson Problem, but it has never been extended to the non-Euclidean problem. This paper uses multidimensional scaling to first project the points from a non-Euclidean space into a Euclidean space, enabling the generation of a convex hull that initializes the algorithm. To evaluate the proposed algorithm, non-Euclidean spaces are created by adding separators to the TSPLIB data-set, or by using the L1 norm as a metric.

Mithun Goutham, Riccardo DalferroNucci, Stephanie Stockar et al.

Accurately estimating decision boundaries in black box systems is critical when ensuring safety, quality, and feasibility in real-world applications. However, existing methods iteratively refine boundary estimates by sampling in regions of uncertainty, without providing guarantees on the closeness to the decision boundary and also result in unnecessary exploration that is especially disadvantageous when evaluations are costly. This paper presents $\varepsilon$-Neighborhood Decision-Boundary Governed Estimation (EDGE), a sample efficient and function-agnostic algorithm that leverages the intermediate value theorem to estimate the location of the decision boundary of a black box binary classifier within a user-specified $\varepsilon$-neighborhood. To demonstrate applicability, a case study is presented of an electric grid stability problem with uncertain renewable power injection. Evaluations are conducted on three test functions, where it is seen that the EDGE algorithm demonstrates superior sample efficiency and better boundary approximation than adaptive sampling techniques and grid-based searches.