The Physics Behind Brain-Computer Interfaces
By Dhruvika Srivastava XE
The human brain is one of nature’s most beautiful and complex creations. With roughly 86 billion neurons interconnected by trillions of synapses, our brains process astonishing amounts of information every second. Imagine, then, attempting to measure, extract, and interpret all the signals in the brain in real time—at the speed of thought. Tapping into the brain might once have been solely in the realm of science fiction—from X-Men to The Matrix. Today, however, scientists can connect the human brain to computers, allowing people to control robotic limbs, move computer cursors, and even translate neural activity into text—all using thought alone.
A brain-computer interface is a system that functions as a bridge between the brain and an external device, usually a computer. BCIs collect, analyse, and translate electrical signals from your brain into commands that can be understood and executed by a computer. In other words, BCIs allow you to control an application or a device using only your mind. Thanks to a combination of physics, neuroscience, biomedicine, and technology, BCIs can change the lives of people with serious medical conditions. They also have applications across robotics, neuroscience, technology, gaming, and computing.
Suppose you want to turn on a lamp. Normally, your brain decides what to do, sends electrical signals to the muscles in your arm and hand, and your finger presses the switch. Finally, the lamp responds. Brain-computer interfaces bypass this middle step. Instead of relying on muscles, they detect the brain's electrical activity, identify the user's intention, and send commands directly to the device.
Neurons in the brain communicate using electrochemical signals. When a neuron is activated, it generates an electrical impulse that travels along the neuron. At the synapse, this signal triggers the next neuron, allowing information to rapidly propagate through neural networks. This electrical activity arises from the movement of charged ions such as sodium (Na⁺), potassium (K⁺), calcium (Ca²⁺), and chloride (Cl⁻) across the neuron's membrane. This movement creates tiny voltage differences known as action potentials, which travel along neurons and carry information throughout the nervous system. At its core, this process follows the same principles of electricity and electromagnetism studied in physics. A single neuron on its own generates only a tiny electrical signal, but the collective activity of neurons within these networks produces enough electrical activity to be detected outside of the head. We can measure this electrical activity in multiple ways.
One of them is by placing special sensors called electrodes onto the head. To capture activity from different parts of the brain, multiple electrodes are placed on a cap, headset, or headband. This method of measuring the brain's electrical activity externally is called electroencephalography, or EEG. EEG does not record the activity of individual neurons. Instead, it measures the combined activity of millions of neurons, which appears as rhythmic brain waves. These waves are classified into alpha, beta, theta, and delta bands, each associated with different mental states such as relaxation, concentration, or sleep. The electrical signals detected by EEG or implanted electrodes are incredibly weak, typically measuring only a few microvolts (μV)—about a millionth of a volt. Because these signals are so faint, they can easily be overwhelmed by electrical interference from muscle movements, blinking, nearby electronic devices, or even power lines.
To make these signals useful, BCIs first pass them through amplifiers, which increase their strength without changing the information they carry. The signals are then processed using digital filters that remove unwanted electrical noise while preserving the brain activity researchers want to analyse. Once cleaned, the analogue electrical signals are converted into digital data that computers can interpret. Machine learning and artificial intelligence algorithms then identify patterns associated with different thoughts or intentions, allowing the computer to execute the user's intended action.
Another technique used to study brain activity is magnetoencephalography (MEG). While EEG measures the tiny voltage differences produced by neurons, MEG detects the extremely weak magnetic fields generated by those same electrical currents. These magnetic fields are incredibly small—billions of times weaker than Earth's magnetic field—so MEG systems require highly sensitive superconducting sensors known as SQUIDs (Superconducting Quantum Interference Devices). These instruments are among the most sensitive magnetic detectors ever built and operate at extremely low temperatures using liquid helium. Many MEG laboratories are built inside magnetically shielded rooms, which act similarly to Faraday cages by blocking external electromagnetic interference. Without this shielding, magnetic fields produced by electrical equipment, vehicles, or even elevators could overwhelm the tiny signals generated by the brain.
Because the skull distorts electrical signals more than magnetic fields, MEG can often provide more precise information about where brain activity originates. However, MEG equipment is extremely expensive, bulky, and usually limited to hospitals and research laboratories, making EEG the more practical choice for most BCI applications.
Both these methods are examples of Non-invasive methods used in BCIs. This means that the sensors remain outside the skull and do not require surgery, but this isn't the only way to measure brain activity. Invasive BCIs involve surgically implanting sensors directly onto the surface of the brain. These systems also measure the electrical activity of neurons, but because the electrical activity doesn't have to travel through bone, skin, and hair to get to the sensor, the recorded activity is much stronger. Invasive BCIs are being studied because these stronger signals could potentially allow us to develop more accurate or complex BCI systems than those that record signals from outside of the scalp, like EEG-based BCIs. But such surgical implantations of electrodes are risky, as they could induce haemorrhages or infections.
Another challenge is that researchers still do not fully understand the long-term effects of implanted electrodes on brain tissue. It is also uncertain how long these implants can continue functioning reliably inside the brain. All this means that electrical implants in their current state cannot safely and reliably help the millions who would benefit from them. In fact, human implantations are carried out only when all other treatment fails, or on an experimental basis – for some 50 individuals worldwide with severe limitations such as paralysis – where the chance to improve a poor quality of life outweighs the dangers. However, non-invasive BCIs such as EEG remain far safer, more affordable, and easier to use, making them the most widely researched and accessible type of brain-computer interface today.
Over the last 25 years, BCIs have allowed paralysed people to operate computers by thought alone. They have restored speech after it has been lost due to a stroke; have allowed those with missing or paralysed limbs to function again or helped them to operate robotic arms and hands. Technologies like EEGs used in BCIs have diagnosed epilepsy and other neurological conditions, and mitigated them for tens of thousands of people. They’ve even shown promise for restoring sight to the blind. Recent advances by companies such as Neuralink have renewed public interest in invasive BCIs, although these technologies remain largely experimental.
Although Brain-Computer Interfaces remain an emerging technology, they demonstrate how the principles of physics can be applied to one of biology's greatest mysteries—the human brain. From detecting electrical impulses just millionths of a volt in strength to measuring magnetic fields billions of times weaker than Earth's, BCIs reveal that understanding the laws of physics may ultimately help us better understand ourselves.