Prosthetic limbs, cochlear implants, and pacemakers take this a step further, modifying the human body with technology. These advances have drastically improved the quality of life of millions of patients world wide. As the power of modern technology and our understanding of human physiology grow, scientists continue to develop models in which technology can supplement a loss of biological function due to disease, genetic disorder, or injury.
With the development of Brain-machine interface (BMI) devices, scientists have found a way to tap into the complex signaling of the human brain, and integrate technology seamlessly into human function. These spectacular advancements that seem to bring the world of science fiction into reality have applications beyond sending hands free emails with simply the power of thought, or changing the television channel with out moving searching for the remote. Researchers hope that the continued development of BMIs will provide assistance for patients suffering from paralysis by decoding movement-related neural signals. Computer software will then integrate these signals to guide computer cursors or prosthetic limbs.
Jose Carmena and colleagues have been investigating Brain-machine interfaces as a means of communication for patients with severe motor disabilities. One type of interface uses functional near-infrared spectroscopy (fNIS) to measure blood flow changes caused by neuron firings. This method avoids the potentially dangerous procedure of implanting electrodes in the brain, and cuts down on electrical noise. fNIS uses light with a wavelength of 650-1000 nm emitted from a device placed on the scalp. These waves diffuse through the skull and grey matter. Some of the waves that pass through the cortical region of the brain are absorbed by oxygenated and deoxygenated hemoglobin (HbO and HbR). The waves admitted by excited photons from HbO and HbR are picked up by receptors attached to the scalp nearby. Due to differing absorption coefficients, computer software can then calculate the changing concentrations of HbO and HbR. This information is used to map blood flow changes in capillary networks related to neuron firing.
This method of signal acquisition, which bypasses the peripheral nervous system, can be used to intercept and interpret neural signals in patients suffering from motor disabilities. In fact, fNIS methods were able to decode the yes-or-no responses of 70% of ALS patients who had lost the ability to control all or most voluntary movement. However, development of a more accurate decoding system is necessary for a BMI to be implemented for more complicated communication, or the control of a prosthetic limb.
While scientists are working to better understand the brain’s signaling and apply this to the control of prosthetics, it seems that the brain is adapting to better communicate with machines. Carmena and colleagues reported that when BMI decoders do not accurately interpret neural signals, neural activity is actually modified. This was observed in trials where human subjects used a BMI connection to control an on-screen cursor to complete tasks. When the decoder did not correctly model the subject’s neural activity, neural adaptation was seen to occur. This meant that when a subject wanted to move an on-screen cursor but was not able to, their brain adapted to send the kind of signals that the decoder could model.
These findings are particularly interesting because it means that instead of creating highly advanced decoders capable of interpreting complicated neural signaling, researchers can instead focus on teaching the brain to use simple decoders. There is so much that researchers do not know about the brain and it’s vastly complicated signaling, yet we are beginning to find that it is not necessary to understand it all to harness its power. In the very near future, we can expect to see BMI technology revolutionizing patient care on a large scale, and making its way into our daily lives.
Shenoy, K. V., & Carmena, J. M. (2014). Combining Decoder Design and Neural Adaptation in Brain-Machine Interfaces. Neuron, 84(4), 665-680.