Can You Read My Mind?
Acquiring signals for neuroprosthetics
Now that summer is in full swing, the onslaught of blockbuster sci-fi movies and books has arrived. Much to the chagrin of pedigreed critics, alien technology coupled with special effects is a common formula for popular acclaim. Daring explorers discover a hidden trove of alien artifacts and, recognizing the portent to improved quality-of-life, the good guys race to understand its function while the bad guys battle to harness it for power and dominion over others. As the aliens are often from a similar universe, common elements such as electrical potential, current, resistance and frequency are easily recognized by our scientists. But, without a fundamental understanding of their linguistics, we have no idea how the logic of the circuitry converts input into output or what the output is meant to control. We can dissect the whole into its parts, but what is needed is a working system that permits output to be observed in response to specific input — a systematic, rather than an analytical approach.
This formula for good science fiction also works for great science fact. We have an alien artifact right between our ears — the mammalian brain. There are entire disciplines dedicated to the study of how humans develop, learn, interact and degenerate. However, only recently do we have the tools and techniques required to investigate the physical operation of the brain. One such research group, lead by Professor John Donoghue at Brown University in Rhode Island, is investigating the interpretation of neural activity in the relatively new field of neurotechnology. Previous studies have discovered specific regions of the brain that are responsible for voluntary behavior, including the primary motor cortex (MI) that generates signals for the brainstem and spinal systems that deliver commands to the arms, legs and face. If these signals can be measured and deciphered using modern technology, nerve networks damaged by injury or disease may be replaced by an electronic counterpart, known as a Neuro Motor Prostheses (NMP) that would re-route the messages to the muscles or replacement limbs.
The first task is measuring output from the MI neurons. When a single neuron fires, or “spikes,” it produces an electrical potential as a result of the firing of other upstream neurons in its neural network. Much like frequency modulation (FM) communication, the number of spikes as a function of time in multiple neurons of the MI encodes information for direction, speed, position and forces on the limbs. Non-invasive sensors have been developed to measure neuron potential. However, the spikes become spatially and temporally averaged as they cross the skull and do not provide the high-frequency information from individual neurons necessary to interpret voluntary intent. Several groups are developing invasive micro array sensors made of biocompatible and biostable material that extend 1 to 2 mm into the cortical tissue and are able to detect spikes at 10s of kHz. These sensors have been embedded into the MIs of monkeys and their output recorded with simultaneous measurements of actual limb movement.
Even after the signals are acquired, the task of interpreting the information is formidable. Donoghue’s group has developed a Switching Kalman Filter (SKF) to analyze the sequences of noisy spikes detected by the micro array sensors. A traditional discrete Kalman Filter (DKF) can be used to minimize the mean-square error between noisy measured data and a model parameter. Whereas linear regression can retrieve best-fit model values from a group of previously collected measurements, the DKF does so in real time by adjusting the model value in response to differences in predicted and measured subsequent values. Unfortunately, the DKF assumes the true signal is a single, steady-state value that varies slowly in time. NMP sensors have shown neurons communicate using a rapid sequence of pulse frequencies to coordinate the position, velocity and acceleration of the limb throughout the full range of motion. The SKF utilizes a Markov chain analysis to discover the hidden patterns of pulse sequences sent by the MI to the muscles. This is akin to using this hybrid Hidden Markov Model (HMM) – SKF to interpret the sequences of alphanumeric characters sent by an alien GPIB or RS-232 communication system. Once the HMMs are discovered, the NMP signals can be interpreted and used to stimulate muscle tissue, control human-computer interfaces (HMIs) or actuate robotic limbs.
The Christopher Reeve Paralysis Foundation has increased awareness of spinal cord injury and the debate over stem cell research. John Donoghue and his colleagues have recently commercialized their NMP research as Cyberkinetics Neurotechnology Systems and are transitioning their technology to human patients in clinical trials to investigate treatment of spinal cord injury, brain stem trauma, amyotrophic lateral sclerosis, muscular dystrophy and stroke. Perhaps solutions using our own developing technology are closer than discovering aliens?