IEEE Newsletter

Jose M. Carmena is an Associate Professor of Electrical Engineering and Neuroscience at the University of California-Berkeley, and Co-Director of the Center for Neural Engineering and Prostheses at UC Berkeley and UC San Francisco. His research program in neural engineering and systems neuroscience is aimed at understanding the neural basis of sensorimotor learning and control, and at building the science and engineering base that will allow the creation of reliable neuroprosthetic systems for the severely disabled. Dr. Carmena received the B.S. and M.S. degrees in electrical engineering from the Polytechnic University of Valencia (Spain) in 1995 and the University of Valencia (Spain) in 1997. Following those he received the M.S. degree in artificial intelligence and the Ph.D. degree in robotics both from the University of Edinburgh (Scotland, UK) in 1998 and 2002 respectively. From 2002 to 2005 he was a Postdoctoral Fellow at the Department of Neurobiology and the Center for Neuroengineering at Duke University (Durham, NC). He is senior member of the Institute of Electrical and Electronics Engineers (IEEE), and member of the Society for Neuroscience, and the Neural Control of Movement Society. Dr. Carmena has been the recipient of the Bakar Fellowship (2012), the IEEE Engineering in Medicine and Biology Society Early Career Achievement Award (2011), the Aspen Brain Forum Prize in Neurotechnology (2010), the National Science Foundation CAREER Award (2010), the Alfred P. Sloan Research Fellowship (2009), the Okawa Foundation Research Grant Award (2007), the UC Berkeley Hellman Faculty Award (2007), and the Christopher Reeve Paralysis Foundation Postdoctoral Fellowship (2003).

Brain-Machine Interfaces - A new research avenue for cybernetics and systems science

Brain-Machine Interfacing (BMI) is about transforming thought into action, or conversely, sensation into perception. This young interdisciplinary field has grown tremendously during the last decade through the advent of multi-electrode recording technology, and has led to impressive demonstrations of neural control of external devices by rats, followed by monkeys and finally humans in a short span of 10 years. Moreover, this technology has the potential to improve the quality of life for millions of people suffering from spinal cord injuries, stroke and other neurological disorders. In this talk we will review the field of cortical BMIs from an application point of view, with examples from experimental results of the impressive adaptive capabilities displayed by the mammalian brain. Specifically, we will show that monkeys can learn to use their brain activity sampled by implanted microelectrode arrays to produce two distinct types of movements in an artificial actuator, reaching and grasping, even in the absence of overt arm movements. Moreover, we will provide evidence from recent results in our laboratory showing how long-term use of a BMI is associated with the formation of a cortical map for prosthetic function that resembles a putative memory engram. Finally, we will discuss examples of interesting problems that the field of BMI brings to the cybernetics and system science communities. We believe that the impact of this technology in the clinical realm will drive neural technology to the next level: augmentation of sensory, motor and cognitive capabilities in healthy subjects. Ultimately, this technology will impact society in many different ways, allowing humans to have direct wireless communication (internet, sensor networks, mobile devices, etc) and interaction (control of artificial devices) with the real world.

Brain-Machine Interfaces - A new paradigm for studying brain function

Two major technological developments during the last decade triggered a paradigm shift in the quest of understanding the brain: chronic multi-electrode recordings and brain-machine interfaces (BMI). Chronic multi-electrode recordings are a powerful tool to study brain function by analysis of spatiotemporal patterns of neural activity. This technique allows recording from large populations of neurons from multiple areas of the brain simultaneously and for long periods of time. This is paramount for studying the spatio-temporal patterns of neural activity and quantifying the neurophysiological changes occurring in cortical networks while subjects perform sensorimotor tasks in both 'manual' and 'prosthetic' control modes of operation. BMI is a new paradigm which contends that a subject can perceive sensory information and enact voluntary motor actions through a direct interface between the brain and an artificial actuator in virtually the same way that we see, walk or grab an object with our own natural limbs. This technology has the potential to improve the quality of life for millions of people suffering from spinal cord injuries, stroke and other neurological disorders.

Moreover, we believe BMI technology will play a key role in the quest for understanding how the brain's architecture and neural circuitry gives rise to its remarkable abilities in perception, cognition, learning, and motor function, which far exceed the abilities of modern digital computers. In this talk, we will discuss the implications of the BMI paradigm for elucidating brain function. We will present recent results from our laboratory showing evidence for long-term consolidation of prosthetic motor memory in non-human primates. This 'cortical map' for prosthetic function resembles a putative memory engram in that it is retained, readily recalled and resistant to interference. The implications of this finding are paramount for the design of neuroprosthetic devices, suggesting that they could be controlled through effortless recall of such a motor memory in a manner that mimics the natural process of skill acquisition and motor control.