Neurotechnology
8 min read
By BioBuilt Editorial
For decades, severe spinal cord injuries, strokes, and neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) have shared a devastating consequence: the brain remains capable of forming intentions, but those signals can no longer reach the muscles needed to carry them out.
Brain–computer interfaces (BCIs) aim to bridge that gap.
Rather than repairing damaged nerves, BCIs record neural activity directly from the brain and translate those electrical signals into commands that can control computers, communication devices, or robotic systems. What once belonged to science fiction is now entering clinical research, where biomedical engineers, neuroscientists, and physicians are exploring how these systems can restore communication and independence for people living with paralysis.
Listening to the Brain
Every movement begins as electrical activity within networks of neurons. Whether reaching for a glass of water or moving a computer cursor, the brain generates patterns of electrical signals long before muscles respond.
A brain-computer interface records these signals using electrodes and sends them to software that attempts to decode the user’s intended movement. Modern BCIs increasingly rely on machine-learning algorithms that improve their ability to recognize patterns associated with specific intentions, such as moving a cursor or selecting letters on a screen.
The challenge is enormous. Millions of neurons may contribute to even the simplest movement, producing overlapping electrical activity that must be interpreted in real time.
Two Different Engineering Philosophies
As clinical BCI research accelerates, two leading approaches have emerged. Both pursue the same goal of restoring communication and independence, though they differ in how they access the brain.
Neuralink: Recording Directly from the Cortex
Neuralink’s system is built around one core assumption: more detailed neural data produces better control.
Its implant, called the N1, sits inside the skull and contains 1,024 electrodes distributed across 64 flexible polymer threads. These threads are extremely thin, which to put into perspective are on the scale of micrometers, and are designed to be inserted into the cerebral cortex, where they can record activity from neurons involved in planning movement.
This design prioritizes signal resolution by recording closer to individual neurons which ultimately allows the system to capture richer electrical detail than surface-level recordings. On the other hand, inserting structures this small into the brain introduces a major engineering challenge. The cortex contains dense networks of blood vessels, and even slight damage during implantation can reduce signal quality or cause biological complications.
To address this, Neuralink developed a custom surgical robot designed to insert each thread with micron-level precision. Using real-time imaging, the robot identifies blood vessels and places electrodes while avoiding visible vascular structures. The process allows hundreds of threads to be implanted in a single procedure.
Once implanted, the electrodes detect tiny voltage changes generated by nearby neurons firing. These signals are transmitted wirelessly to an external device, where decoding algorithms translate them into digital commands.
The advantage of this approach is precision. High electrode density produces detailed neural recordings, which can support fine cursor control and potentially more complex digital interaction in the future. The trade-off is that it requires invasive brain surgery and permanent implantation within neural tissue.
Synchron: Accessing the Brain Through Blood Vessels
Synchron approaches the same problem from a different angle: instead of entering brain tissue, it uses the body’s existing blood vessels as a pathway to the brain.
Its device, the Stentrode™, is a self-expanding metal mesh stent lined with electrodes. Rather than requiring open-skull surgery, it is delivered through a catheter inserted into a vein in the neck and guided through the vascular system until it reaches a blood vessel near the motor cortex.
Once in place, the device expands gently against the vessel wall. From this position, it records local field potentials, the combined electrical activity of large populations of neurons rather than signals from individual cells.
This difference in recording location defines its trade-off. Since it sits outside brain tissue, the signal is lower resolution than intracortical implants like Neuralink’s. However, the procedure is significantly less invasive, relying on techniques already used in standard endovascular surgery.
The implanted device connects to a small transmitter under the skin, which sends neural data wirelessly to an external computer. There, decoding software translates neural patterns into digital commands.
Synchron’s COMMAND clinical study has demonstrated that participants can use the system to perform digital tasks such as texting, navigating computers, and controlling smart-home technology.
Decoding Thought
The most difficult part of a brain-computer interface is not recording signals, but interpreting them. The electrical signals themselves are not commands like “move left” or “click.” Instead, they consist of complex patterns generated by thousands of neurons firing simultaneously.
Machine-learning algorithms analyze these patterns, gradually learning how each individual’s neural activity corresponds to intended movements. As users continue interacting with the system, the algorithms can improve their performance, allowing communication to become faster and more accurate over time.
In many ways, modern BCIs represent a partnership between human neuroplasticity and artificial intelligence: the user learns how to generate consistent neural patterns, while the computer learns how to interpret them.
Looking Ahead
Brain–computer interfaces remain an active area of clinical research, and many important questions remain regarding long-term safety, durability, accessibility, and performance. Nevertheless, recent clinical studies demonstrate that BCIs are already capable of restoring meaningful digital interaction for some individuals living with paralysis.
For biomedical engineers, these devices represent far more than sophisticated electronics. They combine neuroscience, electrical engineering, computer science, robotics, and artificial intelligence to address one of medicine’s most difficult challenges: reconnecting intention with action.
As these technologies continue to mature, the goal is not simply to build better hardware. It is to give people back the ability to communicate, work, and interact with the world—one neural signal at a time.
References
Neuralink. PRIME Study. https://neuralink.com
Synchron. COMMAND Clinical Trial. https://synchron.com
Willett, F. R., et al. “A high-performance speech neuroprosthesis.” Nature (2023).
Moses, D. A., et al. “Neuroprosthesis for Decoding Speech in a Paralyzed Person.” New England Journal of Medicine (2021).
Nature Reviews Neurology. Reviews on brain–computer interfaces and neurotechnology.
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