Miles Johnson

A feedback information-theoretic approach to the design of brain-computer interfaces

This article presents a new approach to designing brain–computer interfaces (BCIs) that explicitly accounts for both the uncertainty of neural signals and the important role of sensory feedback. This approach views a BCI as the means by which users communicate intent to an external device and models intent as a string in an ordered symbolic language. This abstraction allows the problem of designing a BCI to be reformulated as the problem of designing a reliable communication protocol using tools from feedback information theory. Here, this protocol is given by a posterior matching scheme. This scheme is not only provably optimal but also easily understood and implemented by a human user. Experimental validation is provided by an interface for text entry and an interface for tracing smooth planar curves, where input is taken in each case from an electroencephalograph during left- and right-hand motor imagery.

A convex approach to inverse optimal control and its application to modeling human locomotion

Inverse optimal control is the problem of computing a cost function that would have resulted in an observed sequence of decisions. The standard formulation of this problem assumes that decisions are optimal and tries to minimize the difference between what was observed and what would have been observed given a candidate cost function. We assume instead that decisions are only approximately optimal and try to minimize the extent to which observed decisions violate first-order necessary conditions for optimality. For a discrete-time optimal control system with a cost function that is a linear combination of known basis functions, this formulation leads to an efficient method of solution as an unconstrained least-squares problem. We apply this approach to both simulated and experimental data to obtain a simple model of human walking trajectories. This model might subsequently be used either for control of a humanoid robot or for predicting human motion when moving a robot through crowded areas.

A Brain–Machine Interface to Navigate a Mobile Robot in a Planar Workspace: Enabling Humans to Fly Simulated Aircraft With EEG

This paper presents an interface for navigating a mobile robot that moves at a fixed speed in a planar workspace, with noisy binary inputs that are obtained asynchronously at low bit-rates from a human user through an electroencephalograph (EEG). The approach is to construct an ordered symbolic language for smooth planar curves and to use these curves as desired paths for a mobile robot. The underlying problem is then to design a communication protocol by which the user can, with vanishing error probability, specify a string in this language using a sequence of inputs. Such a protocol, provided by tools from information theory, relies on a human users ability to compare smooth curves, just like they can compare strings of text. We demonstrate our interface by performing experiments in which twenty subjects fly a simulated aircraft at a fixed speed and altitude with input only from EEG. Experimental results show that the majority of subjects are able to specify desired paths despite a wide range of errors made in decoding EEG signals.

Inverse optimal control for deterministic continuous-time nonlinear systems

Inverse optimal control is the problem of computing a cost function with respect to which observed state and input trajectories are optimal. We present a new method of inverse optimal control based on minimizing the extent to which observed trajectories violate first-order necessary conditions for optimality. We consider continuous-time deterministic optimal control systems with a cost function that is a linear combination of known basis functions. We compare our approach with three prior methods of inverse optimal control. We demonstrate the performance of these methods by performing simulation experiments using a collection of nominal system models. We compare the robustness of these methods by analysing how they perform under perturbations to the system. To this purpose, we consider two scenarios: one in which we exactly know the set of basis functions in the cost function, and another in which the true cost function contains an unknown perturbation. Results from simulation experiments show that our new method is more computationally efficient than prior methods, performs similarly to prior approaches under large perturbations to the system, and better learns the true cost function under small perturbations.

Remote teleoperation of an unmanned aircraft with a brain-machine interface: Theory and preliminary results

This paper presents an interface that allows a human pilot to remotely teleoperate an unmanned aircraft flying at a fixed altitude with input only from an electroen-cephalograph (EEG), which is used in this case to distinguish between left- and right-hand motor imagery in the brain. The approach is to construct an ordered symbolic language for smooth planar curves and to use these curves as desired paths for the aircraft. The underlying problem is then to design a communication protocol by which the pilot can, with vanishing error probability, specify a string in this language using a sequence of bits sent through a binary symmetric channel in the presence of noiseless feedback. Such a protocol is provided by the combination of arithmetic coding as a method of lossless data compression with posterior matching as a capacity-achieving channel code. Preliminary hardware experiments demonstrate the feasibility of this approach.

Querying the user properly for high-performance brain-machine interfaces: Recursive estimation, control, and feedback information-theoretic perspectives

We propose a complementary approach to the design of neural prosthetic interfaces that goes beyond the standard approach of estimating desired control signals from neural activity. We exploit the fact that the for a users intended application, the dynamics of the prosthetic in fact impact subsequent desired control inputs. We illustrate that changing the dynamic response of a prosthetic device can make specific tasks significantly easier to accomplish. Our approach relies upon principles from stochastic control and feedback information theory, and we illustrate its effectiveness both theoretically and experimentally - in terms of spelling words from a menu of characters using binary surface electromyography classification.

Policies for neural prosthetic control: Initial experiments with a text interface

Neural interfaces use estimates of brain or muscle activity to generate control inputs for a prosthetic device. Most previous work focuses on estimating neural activity more accurately. This paper focuses on generating bette control inputs. It shows that changing the dynamic response of a prosthetic device can make specific tasks easier to accomplish. It also presents experimental results for which neural activity is measured using surface electromyography, the prosthetic is a 1-D cursor, and the task is to spell words from a menu of characters.

Development of a fixed wing multi-role unmanned aircraft vehicle research testbed

This paper describes the design and construction of a fixed-wing multi-role unmanned aerial vehicle (UAV) that has served as a platform for research on (a) human-machine interface based teleoperation, (b) new autonomous control algorithms, and (c) system identification of aircraft performance parameters. These tasks require capabilities that generalize to a wide range of small scale UAV research. In particular, we believe that sharing our design and construction approaches can benefit the research community. An important criterion in development of a research model is its ability to support testing and development of new technologies in a time- and cost-effective manner. Our work achieved this and can perform multiple tasks without transitioning or replacing hardware. The platform itself is constructed out of commercial off the shelf components, in order to decrease development time and costs, and is also capable of performing aggressive maneuvering, which is unusual for research UAVs at the 1.8-meter wingspan scale. The platform construction details and code are available and can serve as the basis for development of future unmanned aerial vehicles by the research community. Preliminary flight tests provide proof of concept.

Real time localization and 3D depth estimation across disparate sensing systems: Toward hazard aware and tele-immersive spaces

This paper addresses the problems of real-time localization and 3D depth estimation across disparate sensing systems. The sensing systems include wireless microelectromechanical systems (MEMS) sensor networks, such as MICA sensors by Crossbow Inc., radio frequency identification (RFID) tags and cameras that capture a variety of spectra. Some of the sensing is adaptive in time and space by using a remotely controlled robot for the sensor deployment. The motivation for integrating and analyzing multiple sensing systems and spectral modalities comes from the fact that in many applications a single sensing system or modality does not lead to robust and accurate performance. In this work we design systems for localization using radio frequency identification (RFID) tags and real time 3D depth estimation from stereo vision in order to incorporate power constraints imposed on deployment of battery-operated wireless MICA sensors. The resulting methods are applied to the development of (a) hazard aware spaces (HAS) to alert people in events of fire, and (b) tele-immersive spaces (TEEVE) to enable remote collaborations, training and art performances. The novelty of our work lies in the power efficient deployment of wireless sensors for location aware applications by combining multiple sensors with advanced signal and image processing algorithms.