METACOMP

A. Abstract

The ability to learn a person's intentions by "reading" his or her brain activity has always been a fascinating theme in the realm of science fiction. Yet, recent advances in brain research indicate that this idea may not be so far-fetched after all. Moreover, while the ability to control brain function at first sight appears frightening, it may also turn out to be a blessing for mankind: A benign usage of such knowledge has the potential to relieve various forms of disabilities. Thus, one of the exciting venues of current research is the develo pment of motor neural prostheses, which will read brain activity and use the output to control the movements of a paralyzed limb or prosthesis. Examining state-of-the-art studies, we find that a crucial problem with this approach is our poor ability to extract the relevant information from the monitored brain activity. The activity provides only partial and "noisy" information about the subject's intentions. Moreover, it changes continuously, due to technical problems such as unstable recordings, or due to the inherent adaptive nature of the brain itself, which modifies its activity to the subjects experience. The METACOMP project is a collaborative effort of teams of scientists with diverse skills in neuroscience coordinated by Prof. Eilon Vaadia in Israel and Prof Ad Aertsen in Germany. Our goal is to be able to construct a system that can efficiently reconstruct desired movements, in a way that best represents the subjects intention. We expect our results to provide a major step toward our long-range objective - to advance towards the usage of these methodologies for improving motor function in amputees, patients who suffer spinal cord injury, Parkinson disease or other motor disorders that leave the motor parts of the brain intact. We emphasize that, we do not expect implementation in human subjects within the first time frame (5 years) of this project. Yet, the prospects for future application are good. Our work is at its early phase but has already yielded preliminary exciting results. Our first efforts in the last year were to optimize extraction of relevant information from neural activity. We made first steps in this direction in two ways. First, we improved existing computational tools to extract information from the neuronal activity1,2 and second, we found how one can improve the brain activity by specific learning3. These results give more hopes to understand what the brain "wants" to tell the moving limb.

B. Main lines of research

There are at four main directions of research that are essential for achieving the ambitious goal of brain-driven robotic devices.

(1) Develop techniques for simultaneous recordings of large numbers of neurons as reliably as possible for prolonged periods of time. While various types of microelectrode arrays are under development and in use, the problem of the recording quality is still not solved. This work will not be done in Vaadia lab since it requires very expensive methodologies. However, Dr. Vaadia is negotiating with high-tech engineering companies and will be willing to test their products.

(2) Improve our understanding of the neural code. It is still unclear how neuronal activity in motor cortex represents and controls ongoing movements. While some hold that populations of neurons accurately represent the time course of movement kinematics (direction and speed of the limb) others maintain that the motor cortex encodes intrinsic dynamic parameters (joint by joint, muscle by muscle activation). This project will develop and examine additional approaches tonalysis, information extraction and interpretation of neuronal recordings.

(3) Develop algorithms to transform neuronal activity to reconstructed movements. This will serve as the "model" in figure 1. Various algorithms are good candidates for interpreting neuronal activity and prediction of movements. The interdisciplinary center for neural computation is especially gifted with scientists who have already contributed to the efforts to develop improved methodologies. (see example - DDT animation)

(4) Interface robotic devices with the biological system. Several groups have been engaged in experiments to develop the most challenging class of prostheses mentioned above - brain-driven prostheses. While this research is still at its first steps, it is evident that this challenge can be met in the future. Our group will use available robotic devices. One such device is already in our laboratory. We plan to use the knowledge we acquire to control this robot by brain activity that will be recorded from awake monkeys.

C. Outlook

When the first phase of this research is completed (5 years) we plan to have a complete system that allows for controlling a robotic arm in real time by reading brain activity in monkeys. No invasive experiments in humans are planned within the scope of this project. Instead, we rely on psychophysics (behavioral experiments) in humans, electrophysiology in primates, and the development of novel tools for data processing and adaptive control. Clinical tests may, however, be scheduled after the principle has been successfully demonstrated in primates, and after the issue of long-term bio-compatibility of the direct interface has experienced enough progress. Our experiments will provide detailed and quantitative information on the prerequisites that ensure stable and robust performance of the envisaged prosthetic devices. This knowledge will be exploited to design interfaces that allow minimizing the invasive actions taken in future applications for human patients.