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Every action our body performs begins with a thought, and with every thought comes an electrical signal. The electrical signals can be received by the brain-computer interface, consisting of an electroencephalograph (EEG) or an implanted electrode, which can then be translated, and then sent to the performing hardware to produce the desired action. BCI is an alternative system built on artificial mechanisms and acts as a bridge between the brain and external devices. The aim of BCI is to convey human intentions to external devices by directly extracting brain signals. Eventually, the brain and computers would be highly integrated.
Different techniques are used to measure brain activity for BCIs. Most BCIs have used electrical signals that are detected using electrodes placed invasively within or on the surface of the cortex, or noninvasively on the surface of the scalp [electroencephalography (EEG)]. Some BCIs have been based on metabolic activity that is measured noninvasively, such as through functional magnetic resonance imaging (fMRI).
The flexible electrode is the crucial component of the BCI, and is the key for the development of the BCI technology. Recently, with the increasing demands on Brain-Computer Interface, plenty of flexible electrode materials and the structural design for applications in BCI technology have been developed.
According to the electrode position and implantation method, the BCI can be divided into three ways, including non-intrusive electrodes, intrusive electrodes and semi-intrusive electrodes. Three fundamental techniques to interface with the brain are ; non-inasive such as electro-encephalography (EEG), invasive through direct connections and electro-corticography (ECoG), also known as intracranial EEG – a sort of half-way house involving electrodes placed on the brain’s exposed surface, rather than hardwired into the brain itself. Invasive BCI are technologies that provide high resolution but require neurosurgery. They require regulatory approvals, hence manufacturers are less willing to fund clinical trials associated with the approval process.
Non-Invasive BCI have gained popularity in the recent times and are expected to grow at a fast pace in the near future because it provides least discomfort and negligible chance of infection due to electrode use. Progress in non-invasive electroencephalography (EEG)-based brain-computer interface (BCI) research, development and innovation has accelerated in recent years. New brain signal signatures for inferring user intent and more complex control strategies have been the focus of many recent developments. Major advances in recording technology, signal processing techniques and clinical applications, tested with patient cohorts as well as non-clinical applications have been reported, writes Damien Coyle.
Materials for BCI
Ensuring the quality of the collected EEG signals is an imperative prerequisite for BCI technology to obtain good comprehensive performance. The key of electrode lies in the choice of material and structural design.
The inherent high elastic modulus, high density and weak bio-compatibility of traditional electrodes can’t meet the demands of EEG signal acquisition. Current implantable devices are not well matched with body tissues in terms of their mechanical, chemical, and physical properties. The tissues that may be excited or interrogated by implants (e.g., brain, spinal cord, or cardiac muscle) are mechanically compliant, curvilinear, and perform their functions by modulating the flow of ions, Wei-Chen Huang, Haosheng Wu and Christopher J. Bettinger of Carnegie Mellon University (CMU). Conversely, most implantable silicon-based devices are mechanically rigid, and use electrons or holes as their primary information currency.
“These elements of mismatch reduce the overall performance of current implantable technology in three ways. First, the difference in mechanical properties (i.e., the elasticity) can cause local tissue damage that compromises the fidelity of measurements. Second, changing between ionic and electronic transduction decreases the information density and stimulation specificity. Finally, the materials that are typically used in microelectronic implants are susceptible to rapid protein adsorption, which initiates a cascade of local inflammation and scarring. The biological response to the presence of foreign material (such as an implant) can also compromise bidirectional communication.”
Therefore, it’s particularly important to find flexible electrode materials with low elastic modulus, high porosity, wet softness and good bio-compatibility. With high flexibility, suitable mechanical compliance, good bio-compatibility and minimized structural, mechanical and topological differences, they have become the best candidates of BCI technology for brain electrical stimulation and signal acquisition. Under the demands, the materials with high flexibility have become the most promising novel BCI materials, such as polymers, nanomaterials, carbon-based materials, bio-materials, hydrogels, etc
Non-invasive, also known as wearable, doesn’t need to be implanted in the brain. This non-invasive method is less risky and can guarantee security. However, what is urgently needed is the issue of convenience. At present, the most mature application is electroencephalography (EEG), but the equipment is heavy and extremely inconvenient to use. The ideal wearable BCI electrodes should consider the stability of signal and the comfortable of users, which brings new challenges and higher requirements for the design of materials and structures. Theoretically, non-intrusive BCI devices based on flexible electrodes can improve the precision, reliability and sensitivity, making them convenient to wear, complete in function and beautiful in appearance.
The macroscopic flexibility of BCI devices is mainly determined by the electrode materials. At present, there are mainly two kinds of material design routes for flexible electrodes. One is adding conductive substance (metal, carbon-based material, etc) to the flexible matrix, and then forming flexible conductive mixture through physical mixing. For example, carbon-based materials are uniformly dispersed in the polydimethylsiloxane to make flexible electrodes. The other is depositing or plating the conductive material on the flexible substrate, and then patterning it. Due to the stretch ability of flexible materials, flexible electrodes can increase contact area, reduce contact resistance and reduce motion artifacts.
The flexible substrate plays a role in buffering and damping. It’s an important part of the flexible electrode, and also a skeleton during the entire electrode preparation process. Generally, the aforementioned flexible materials mainly include polymers, carbon-based materials, nanomaterials, and the like. Polymers mainly include polyimide, polyurethane, parylene, polydimethylsiloxane, etc. The stiffness of these materials is two orders of magnitude lower than traditional materials (metal, inorganic silicon, etc)
Carbon-based materials mainly include carbon nanotubes, graphene, carbon fibers, etc. Such materials have high flexibility, low density, good electrical and mechanical properties. It is the interconnected porous channels inside the materials that help to achieve the rapid migration of electron and ion, which greatly improves the electrochemical property . As a typical nanomaterial for flexible electrode substrates, with abundant sources, low cost, high bio-compatibility, non-cytotoxicity, high mechanical strength, high degree of polymerization, high crystallinity and ultra-fine structure, nanocellulose is also an ideal material for the preparation of flexible electrodes. Besides, the design of carbon nanofiber materials can also enhance flexibility by adjusting the micro structure, such as introducing pore structure, reducing fiber size, increasing the degree of graphitization, and so on.
Carbon Micro thread electrodes
In 2014, Scientists at University of Michigan have come up with micro thread electrode which is delicate enough not to damage nerve tissue and still resilient enough to last decades. This seven micrometer carbon fiber thread is 100 times thinner than common metal electrodes. It has its tip coated with polymer to pick of signals even from a single neuron.
The electrodes may lead to development of long lasting brain machine interfaces through which paralytic persons could control robotic limbs or computer mouse. However there is still many challenges to overcome like finding ways to insert such fine electrodes.
Carbon nanotube fibers make superior brain electrodes
Carbon nanotube fibers invented at Rice University may provide the best way to communicate directly with the brain. “They’re like extension cords,” said Mehdi Razavi, the director of electrophysiology clinical research at the Texas Heart Institute and the project’s lead investigator. “They allow us to pick up charge from one side of the scar and deliver it to the other side. Essentially, we’re short-circuiting the short circuit.”
The fibers have proven superior to metal electrodes for deep brain stimulation and to read signals from a neuronal network. Because they provide a two-way connection, they show promise for treating patients with neurological disorders while monitoring the real-time response of neural circuits in areas that control movement, mood and bodily functions.
“The brain is basically the consistency of pudding and doesn’t interact well with stiff metal electrodes,” Caleb Kemere, a Rice assistant professor said. “The dream is to have electrodes with the same consistency, and that’s why we’re really excited about these flexible carbon nanotube fibers and their long-term biocompatibility.”
The fibers were created by the Rice lab of chemist and chemical engineer Matteo Pasquali.” We developed these fibers as high-strength, high-conductivity materials,” Pasquali said. “Yet, once we had them in our hand, we realized that they had an unexpected property: They are really soft, much like a thread of silk. Their unique combination of strength, conductivity and softness makes them ideal for interfacing with the electrical function of the human body.” The working end of the fiber is the exposed tip, which is about the width of a neuron. The rest is encased with a three-micron layer of a flexible, biocompatible polymer with excellent insulating properties.
The challenge is in placing the tips. “That’s really just a matter of having a brain atlas, and during the experiment adjusting the electrodes very delicately and putting them into the right place,” said Kemere, whose lab studies ways to connect signal-processing systems and the brain’s memory and cognitive centers.
Kemere foresees a closed-loop system that can read neuronal signals and adapt stimulation therapy in real time. He anticipates building a device with many electrodes that can be addressed individually to gain fine control over stimulation and monitoring from a small, implantable device. The Welch Foundation, the National Science Foundation and the Air Force Office of Scientific Research supported the research.
Stanford University reported to have developed New Silicon-based Chips Could Improve Brain-Machine Interfaces in March 2020
Researchers at Stanford University have developed a new device for connecting the brain directly to silicon-based technologies. While brain-machine interface devices already exist – and are used for prosthetics, disease treatment and brain research – this latest device can record more data while being less intrusive than existing options.
“Nobody has taken these 2D silicon electronics and matched them to the three-dimensional architecture of the brain before,” said Abdulmalik Obaid, a graduate student in materials science and engineering at Stanford. “We had to throw out what we already know about conventional chip fabrication and design new processes to bring silicon electronics into the third dimension. And we had to do it in a way that could scale up easily.”
The device contains a bundle of microwires, with each wire less than half the width of the thinnest human hair. These thin wires can be gently inserted into the brain and connected on the outside directly to a silicon chip that records the electrical brain signals passing by each wire – like making a movie of neural electrical activity. Current versions of the device include hundreds of microwires but future versions could contain thousands. “Electrical activity is one of the highest-resolution ways of looking at brain activity,” said Nick Melosh, professor of materials science and engineering at Stanford and co-senior author of the paper. “With this microwire array, we can see what’s happening on the single-neuron level.”
The researchers tested their brain-machine interface on isolated retinal cells from rats and in the brains of living mice. In both cases, they successfully obtained meaningful signals across the array’s hundreds of channels. Ongoing research will further determine how long the device can remain in the brain and what these signals can reveal. The team is especially interested in what the signals can tell them about learning. The researchers are also working on applications in prosthetics, particularly speech assistance.
The researchers knew that, in order to achieve their aims, they had to create a brain-machine interface that was not only long-lasting, but also capable of establishing a close connection with the brain while causing minimal damage. They focused on connecting to silicon-based devices in order to take advantage of advances in those technologies. “Silicon chips are so powerful and have an incredible ability to scale up,” said Melosh. “Our array couples with that technology very simply. You can actually just take the chip, press it onto the exposed end of the bundle and get the signals.”
One main challenge the researchers tackled was figuring out how to structure the array. It had to be strong and durable, even though its main components are hundreds of minuscule wires. The solution was to wrap each wire in a biologically-safe polymer and then bundle them together inside a metal collar. This assures the wires are spaced apart and properly oriented. Below the collar, the polymer is removed so that the wires can be individually directed into the brain.
Existing brain-machine interface devices are limited to about 100 wires offering 100 channels of signal, and each must be painstakingly placed in the array by hand. The researchers spent years refining their design and fabrication techniques to enable the creation of an array with thousands of channels – their efforts supported, in part, by a Wu Tsai Neurosciences Institute Big Ideas grant.
“The design of this device is completely different from any existing high-density recording devices, and the shape, size and density of the array can be simply varied during fabrication. This means that we can simultaneously record different brain regions at different depths with virtually any 3D arrangement,” said Jun Ding, assistant professor of neurosurgery and neurology, and co-author of the paper. “If applied broadly, this technology will greatly excel our understanding of brain function in health and disease states.”
After spending years pursuing this ambitious-yet-elegant idea, it was not until the very end of the process that they had a device that could be tested in living tissue. “We had to take kilometers of microwires and produce large-scale arrays, then directly connect them to silicon chips,” said Obaid, who is lead author of the paper. “After years of working on that design, we tested it on the retina for the first time and it worked right away. It was extremely reassuring.”
Following their initial tests on the retina and in mice, the researchers are now conducting longer-term animal studies to check the durability of the array and the performance of large-scale versions. They are also exploring what kind of data their device can report. Results so far indicate they may be able to watch learning and failure as they are happening in the brain. The researchers are optimistic about being able to someday use the array to improve medical technologies for humans, such as mechanical prosthetics and devices that help restore speech and vision.
References and Resources also include:
https://www.sciencedaily.com/releases/2020/04/200420125540.htm
https://iopscience.iop.org/article/10.1088/2053-1591/abb857
