Brain-computer interfaces (BCIs) are changing how humans interact with technology, allowing direct communication between the brain and external devices.
Image Credit: Alexander Supertramp/Shutterstock.com
By capturing neural signals, BCIs can help control computers, prosthetics, and more—without physical movement. These devices range from non-invasive wearables to surgically implanted chips, each offering different levels of precision. This article explores how BCIs work, their applications, and their future potential:
- How BCIs capture and interpret neural signals
- The different types of BCIs and their advantages
- Applications in healthcare, assistive technology, and beyond
- Challenges and the future of BCI development
What is a Brain-Computer Interface?
BCIs bridge the gap between thought and action, allowing direct communication between the brain and external devices. These systems capture electrical signals from neurons and translate them into commands that can control computers, prosthetics, or other technology—essentially enabling people to turn thoughts into movement.1-3
BCI technology can take different forms, from non-invasive wearables that rest on the scalp to surgically implanted chips placed under the skull or even within brain tissue. The closer the device is to the brain’s neural network, the clearer and more precise the signal, improving communication between mind and machine.
In essence, a BCI acts as a direct link between the human brain and external hardware, opening up new possibilities for medical applications, assistive technology, and even human augmentation.
Primarily, BCIs benefit individuals with neurological conditions that impair movement but leave cognitive function intact. This includes people with locked-in syndrome, amyotrophic lateral sclerosis (ALS), multiple sclerosis, spinal cord injuries, and other motor impairments caused by aging or trauma. By enabling users to control devices, communicate more easily, and operate robotic or prosthetic limbs, BCIs can significantly improve quality of life.1-3
How do Brain-Computer Interfaces Work?
BCIs are designed to interpret neural activity by mimicking the brain’s natural electrophysiology. Every thought or decision triggers electrochemical signals between neurons, occurring at synapses where neural communication takes place.
To capture these signals, BCIs use electrodes placed near neural networks to detect voltage changes, measuring the frequency and intensity of neural spikes. This data is then processed through neural decoding, where machine learning and artificial intelligence translate brain activity into structured commands.
Essentially, BCIs function like microphones, but instead of detecting sound, they listen for electrical activity—the neural "chatter" between brain cells. Let's break it down even further.
A BCI system operates through three key steps: signal acquisition, signal processing, and device output. These steps form the BCI’s operational framework, which must be adaptable to meet individual user needs.
1. Signal Acquisition
The first step in a BCI system is collecting neural signals using electrodes positioned near specific brain regions. The placement method determines how clear and detailed the signals are:
- Invasive BCIs: Electrodes are implanted directly into brain tissue, providing the most precise and high-resolution signals. This method is commonly used in medical research and for individuals with severe motor impairments.
- Partially invasive BCIs: Electrodes are placed under the skull but do not penetrate the brain tissue. This offers a balance between signal quality and surgical complexity.
- Non-invasive BCIs: Electrodes rest on the scalp, typically using electroencephalography (EEG) to detect brain activity. While this method is the safest and most accessible, it has lower signal resolution due to interference from the skull and surrounding tissue.
Each method affects how effectively the BCI can interpret brain signals, influencing both performance and practical usability.
2. Signal Processing
Once neural signals are captured, they must be processed and interpreted before they can control an external device. This involves several steps:
Feature Extraction: Raw brain signals contain a mix of meaningful activity and background noise. The system identifies key features (such as frequency, amplitude, or phase of brainwaves) that indicate the user’s intent. These features are stored in a feature vector, a structured dataset that simplifies further analysis.
Classification and Decoding: Machine learning algorithms analyze the extracted features to classify patterns and predict the user’s intended action. Common classifiers include:
- Neural networks – Mimic the way the brain processes information, enabling deep learning techniques.
- Linear classifiers – Separate data into distinct categories for decision-making.
- Nearest neighbor classifiers – Compare new data points to previously recorded signals.
- Nonlinear Bayesian classifiers – Use probability models to make predictions.
- Combined classifiers – Integrate multiple models to improve accuracy.
Recent advancements in deep learning allow BCIs to analyze minimally preprocessed brain signals with greater accuracy, improving responsiveness while reducing the need for manual calibration.
3. Device Output
Once classified, the processed signals are converted into commands that control an external device. A modern BCI system’s output typically interacts with:
- Computers – Allowing users to move a cursor, type text, or select icons.
- Prosthetic limbs and robotic systems – Enabling motor-impaired individuals to control artificial limbs.
- Assistive communication devices – Helping individuals with conditions like ALS or locked-in syndrome communicate through text or speech synthesis.
In research settings, users often receive feedback through a visual interface, such as a monitor displaying icons, letter selections, or cursor movements. The integration of real-time feedback improves the accuracy of user intent recognition, making BCI systems more intuitive and responsive.
BCI Types
BCIs are categorized based on how they capture brain signals. The three main types are invasive, non-invasive, and partially invasive. Each approach has different levels of accuracy, safety, and usability, influencing their applications in medical and technological fields.2,3
Invasive BCIs
Invasive BCIs involve surgically implanted electrodes that record signals directly from the brain. Because they capture activity at the single-neuron level, they offer high spatial resolution and a strong signal-to-noise ratio, making them the most precise type of BCI.
Two common approaches include:
- Microelectrode arrays (MEAs): Small electrodes placed in the cortex to track individual neuron activity. These are used for controlling robotic limbs and neuroprosthetic devices.
- Electrocorticography (ECoG): Electrodes placed on the brain’s surface (without penetrating brain tissue). This method provides high signal accuracy while reducing some of the risks associated with deeper implants.
While invasive BCIs provide precise control, they require surgery, which comes with risks such as infections, scarring, and long-term maintenance challenges. They are primarily used in clinical and research settings.
Non-Invasive BCIs
Non-invasive BCIs do not require surgery. Instead, they use external sensors to detect brain activity. Since signals must pass through the skull, they tend to have lower spatial resolution and more interference compared to invasive methods.
Common types include:
- Electroencephalography (EEG): Measures electrical activity using electrodes on the scalp. EEG is widely used due to its portability, affordability, and good temporal resolution, making it suitable for applications like neurorehabilitation and brain-controlled interfaces.
- Magnetoencephalography (MEG): Detects the brain’s magnetic fields. It provides better spatial resolution than EEG but requires specialized environments.
- Functional Magnetic Resonance Imaging (fMRI): Tracks blood flow in the brain to infer neural activity. It offers high spatial resolution but lacks real-time usability due to slow signal processing.
Non-invasive BCIs are used in medical diagnostics, cognitive monitoring, and consumer applications, such as brain-controlled gaming and meditation devices.
Partially Invasive BCIs
Partially invasive BCIs are implanted inside the skull but remain outside the brain’s gray matter. This allows them to pick up stronger signals than non-invasive BCIs while reducing some of the risks associated with fully implanted electrodes.
They offer better signal clarity than EEG and MEG, a lower risk of scar tissue than fully invasive implants, and greater long-term stability than deep-brain electrode systems. By striking a balance between precision and safety, they are being explored for long-term medical applications.
Applications of Brain-Computer Interfaces
BCI technology is being developed for both medical and non-medical applications, with uses ranging from assistive devices to advanced security systems.2
Non-Medical Applications
One of the most well-known non-medical uses of BCIs is gaming, where EEG-based systems can adjust difficulty levels in real-time based on a player’s cognitive state. By detecting changes in engagement or fatigue, BCIs help maintain an immersive gaming experience.
Beyond entertainment, biometric security is another emerging application. Traditional authentication methods like fingerprints or passwords can be compromised, but brainwave-based authentication offers a more personalized and secure alternative. However, EEG-based systems still face practical challenges, such as the need for electrodes, which can limit usability.
Neuromarketing is another growing field, though it raises ethical concerns. BCIs allow researchers to track attention and emotional responses, providing deeper insights into consumer preferences than traditional surveys or focus groups. This data is used to optimize advertising strategies, making ads more engaging and increasing their effectiveness.
Medical Applications
In the medical field, BCIs are used in four key areas: assistance, neurorehabilitation, prevention, and treatment.
- Assistive Technologies: BCIs enable greater independence for individuals with mobility impairments. Systems that control wheelchairs, robotic arms, or smart home devices allow users to interact with their environment using only brain activity.
- Neurorehabilitation: These BCIs are designed to restore lost cognitive or motor function, often in patients recovering from stroke or spinal cord injuries. By reinforcing neural pathways, they help with motor recovery and rehabilitation.
- Early Detection & Prevention: BCIs can detect subtle changes in brain activity, helping with early diagnosis of neurodegenerative diseases like Alzheimer’s and Parkinson’s. This allows for earlier interventions, potentially slowing disease progression.
- Treatment & Therapy: BCIs also support real-time monitoring of neurological conditions, allowing doctors to tailor treatments based on a patient’s brain activity. This is particularly useful for conditions like epilepsy, where adaptive therapies can reduce seizure frequency.
Across both medical and non-medical fields, BCIs are expanding the possibilities of human-computer interaction, offering new ways to enhance daily life, improve accessibility, and personalize healthcare.2
Brain-Computer Interface Technology
Several companies are leading the development of BCI technology, including Neurable, ONWARD Medical N.V., Blackrock Neurotech, and Synchron. These companies are expanding production, investing in research and development, and forming strategic partnerships to improve BCI performance and reliability across various applications.
However, one of the most well-known players in the field is Neuralink, focusing on a fully implantable and cosmetically invisible BCI. Designed to give users hands-free control of computers and mobile devices, Neuralink’s technology aims to restore independence for individuals with severe disabilities while laying the groundwork for broader applications in the future.4,5
At the center of Neuralink’s system is the N1 implant, a neural interface designed to decode movement intention. Encased in a biocompatible, hermetically sealed enclosure, the device is built to withstand harsh physiological conditions while maintaining long-term functionality. It runs on a small, wirelessly rechargeable battery, allowing for continuous use without the need for external connections.
To record neural activity, the N1 implant uses 1024 electrodes embedded in 64 ultra-thin, flexible threads. These threads are designed to minimize tissue damage during implantation and long-term use. The captured signals are processed by custom low-power electronics, which wirelessly transmit data to the Neuralink application, where they are translated into intent-driven actions.
In January 2024, Neuralink conducted its first human implantation, successfully detecting the participant’s neural signals shortly after surgery. The patient was able to control a computer mouse using only their thoughts, and over time, they used the system for various tasks, including playing online chess and Sid Meier’s Civilization VI.
However, in the weeks following the procedure, about 85 % of the implant’s tendrils detached from the brain, weakening the signal strength. To address this, Neuralink’s team had to reconfigure the system, allowing the patient to regain cursor control. This issue underscores one of the biggest challenges in BCI development—ensuring long-term electrode stability within the brain.
Despite this setback, Neuralink continues refining its design to improve durability, reliability, and signal strength, with ongoing research aimed at enhancing the real-world usability of fully implantable BCIs.4,5,6
Future Outlook
As BCI technology advances, its impact is expected to grow across healthcare, communication, and everyday life. Improved neural interfaces could enhance mobility for individuals with disabilities, refine brain-controlled communication tools, and even integrate BCIs into consumer technology for seamless human-computer interaction.
While challenges like signal stability and long-term implantation remain, ongoing research is pushing the field forward. Engineers and neuroscientists are working on more durable electrodes, higher-resolution signal processing, and better wireless connectivity, all of which could improve the reliability and accessibility of BCIs.
BCI technology is already reshaping industries by increasing user autonomy and introducing innovative solutions in both medical and non-medical fields. As research continues, these systems could become more precise, practical, and widely available, offering life-changing benefits across multiple sectors.
Want to Learn More?
Brain-computer interfaces are evolving fast, and we're only beginning to understand what they might make possible. Whether it's restoring mobility, enhancing communication, or even changing how we interact with technology, BCIs could reshape everyday life in ways we haven't fully imagined.
If you're interested in exploring more, here are a few related topics:
References and Further Reading
- Brain-Computer Interfaces [Online] Available at https://researchbriefings.files.parliament.uk/documents/POST-PN-0614/POST-PN-0614.pdf (Accessed on 05 February 2025)
- Sibilano, E. et al. (2023). Brain–Computer Interfaces. Psychophysiology Methods, 203-240. DOI: 10.1007/978-1-0716-3545-2_10, https://link.springer.com/protocol/10.1007/978-1-0716-3545-2_10
- Büyükgöze, S. (2019). The brain-computer interface. International Conference on Technics Technologies and Education, 133-138. DOI: 10.15547/ictte.2019.02.094, https://www.researchgate.net/publication/338938718_THE_BRAIN-COMPUTER_INTERFACE
- Neuralink [Online] Available at https://neuralink.com/ (Accessed on 05 February 2025)
- PRIME Study Progress Update [Online] Available at https://neuralink.com/blog/prime-study-progress-update/ (Accessed on 05 February 2025)
- Jewett, C. (2024) Despite Setback, Neuralink’s First Brain-Implant Patient Stays Upbeat [Online] Available at https://www.nytimes.com/2024/05/22/health/elon-musk-brain-implant-arbaugh.html (Accessed on 05 February 2025)
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