Author: Corbin Lu
Image credit: Illustration by Corbin Lu, 2026
When I first became interested in brain-computer interfaces (BCI), one description appeared again and again: mind reading. I can understand why people use it. It is much easier to imagine a computer reading a thought than to explain how electrodes record tiny electrical signals from the brain (an electroencephalogram; EEG), why the process often has to be repeated, and how the software uses those signals to estimate what the user was focusing on.
The phrase became less convincing to me once I started reading about how one relatively established type of BCI actually works. I am a mathematics student rather than a neuroscience researcher, so I came to the topic as a beginner. What interested me was the difference between the fairly specific process described in research papers and the much more dramatic way BCI is sometimes described outside research.
One common type of BCI uses a brain response called the P300 to help a user select between options on a screen. An early example was the P300 speller. In this system, letters and commands were arranged in a matrix. Rows and columns flashed while the user focused on the character they wanted. When the user recognised the target, the brain could produce an event-related potential, which is a brief change in the EEG linked to a particular event. This response is called the P300 because it usually appears as a positive peak around 300 milliseconds after the target is shown. Since one response can be difficult to separate from background noise, the computer combines information from several flashes to estimate the intended character (Farwell & Donchin, 1988).
Although the underlying technology is complex, one detail is especially important: the possible choices already exist on the screen. The computer is not searching through everything in a person’s mind. The person is deliberately paying attention to one of the options that the interface provides.
I found it easier to understand by reducing the example even further. Imagine that the screen only has two choices, YES and NO. A person chooses an answer and focuses on it as the options flash. Electrodes on the scalp record tiny electrical signals generated by the brain, but the recording can also pick up unwanted signals from blinking, eye movements, tense muscles, and electrical interference. These extra signals are called noise because they can make the P300 response harder to detect (Jiang et al. 2019). The software then analyses the recording and decides which option is most likely to match the pattern it detected.
That description is much less exciting than saying a computer has read someone’s mind, but it is more interesting in another way. A weak and noisy biological signal can still become useful information after several stages of processing.
It also explains why accuracy is not automatically the same for everyone. Guger et al. (2009) tested a P300 interface with 100 participants and found that many could achieve high accuracy after a relatively short period, but performance varied, and some participants could not use the system successfully during the test. This variation is easy to miss when only a single successful demonstration is shown.
Looking at how people actually use the technology also reveals some of its limits. Nijboer et al. (2008) studied a P300 communication system with people with amyotrophic lateral sclerosis (ALS), a disease that damages the nerve cells controlling voluntary movement and gradually causes muscle weakness. Their results showed that communication was possible under the study conditions, even for participants with severe motor impairment, but it was much slower than speaking or typing and was not always accurate.
I do not think those limitations make the technology unimportant. Actually, this was one of the things I had to reconsider while reading. My first instinct was to evaluate communication by comparing it with normal typing or speaking speed. For someone who has very limited voluntary movement, however, even a slower selection system could create a communication route that otherwise would not exist. A technology does not have to look like science fiction to matter.
At the same time, I think it is important not to turn that situation into an inspirational story in which the technology simply solves everything. A P300 system may require calibration and repeated trials, and performance can be affected by the user and the particular interface. A recent systematic review also shows how much visual P300 systems vary in their design, equipment, classification methods, and applications (Kalra et al. 2023).
The term “mind reading” creates another problem because it can describe very different levels of inference. Brown (2024) argues that a system may infer something from brain activity without actually gaining reliable access to the full meaning of a person’s thoughts. A system that estimates whether someone attended to YES instead of NO is doing something very different from retrieving an unspoken sentence exactly as it exists in that person’s mind.
I am still not sure that the term “mind reading” is always completely useless. It can introduce the basic idea that information is being inferred from brain activity. But without an explanation after it, the phrase probably creates more confusion than it solves.
For me, a P300 BCI became easier to understand when I stopped imagining it as a machine listening to an internal voice. It is closer to a chain of steps: the system presents choices, a user deliberately attends to a target, EEG records a response, the signal is processed, and the software estimates which option the user intended to choose.
That process is already impressive without calling it “mind reading.” P300 systems still have clear limits, but they show how small changes in brain activity can be turned into a form of communication. For people who have very limited voluntary movement, even a slow and imperfect system may provide a way to express choices that would otherwise be difficult to communicate.
Article written by Corbin Lu, an undergraduate mathematics student at the University of Waterloo with an interest in brain-computer interfaces and neurotechnology.
Article edited by Priscilla Wong, a recent BSc Biological Sciences (Immunology) (Hons) graduate from the University of Edinburgh and Head Online News Editor for EUSci.
References:
Brown, C. M. L. (2024). Neurorights, mental privacy, and mind reading. Neuroethics, 17(2), 34. https://doi.org/10.1007/s12152-024-09568-z
Farwell, L. A., & Donchin, E. (1988). Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology, 70(6), 510–523. https://doi.org/10.1016/0013-4694(88)90149-6
Guger, C., Daban, S., Sellers, E., Holzner, C., Krausz, G., Carabalona, R., Gramatica, F., & Edlinger, G. (2009). How many people are able to control a P300-based brain–computer interface (BCI)? Neuroscience Letters, 462(1), 94–98. https://doi.org/10.1016/j.neulet.2009.06.045
Jiang, X., Bian, G.-B., & Tian, Z. (2019). Removal of Artifacts from EEG signals: a review. Sensors, 19(5), 987. https://doi.org/10.3390/s19050987
Kalra, J., et al. (2023). How Visual Stimuli Evoked P300 is transforming the Brain–Computer Interface landscape: A PRISMA-compliant Systematic Review. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 1429–1439. https://doi.org/10.1109/TNSRE.2023.3246588
Nijboer, F., et al. (2008). A P300-based brain–computer interface for people with amyotrophic lateral sclerosis. Clinical Neurophysiology, 119(8), 1909–1916. https://doi.org/10.1016/j.clinph.2008.03.034
Leave a Reply