Diving Deeper into BCI Technologies: Non-Invasive vs. Invasive Modalities

Introduction: After exploring the basics of Brain-Computer Interfaces in our previous blog, it’s time to take a closer look at the different types of BCIs. Specifically, we’ll be comparing non-invasive and invasive approaches, discussing their respective advantages, limitations, and use cases.

Understanding Non-Invasive BCIs: Non-invasive BCIs are designed to capture brain signals without the need for surgical procedures. These systems are popular because they are safer and easier to deploy compared to invasive methods. The most common non-invasive technique is EEG (Electroencephalography), which involves placing electrodes on the scalp to measure brain activity.

• EEG (Electroencephalography): EEG is the foundation of non-invasive BCIs. It measures the brain’s electrical activity through electrodes on the scalp. While EEG is non-invasive and relatively inexpensive, it is prone to noise and has limited spatial resolution. Despite these limitations, EEG is widely used in research and clinical applications due to its accessibility and ease of use.
• fNIRS (Functional Near-Infrared Spectroscopy): fNIRS is another non-invasive technique that measures brain activity by detecting changes in blood oxygenation. This method offers better spatial resolution than EEG but is slower in capturing brain signals. fNIRS is particularly useful in monitoring cortical activity during cognitive tasks.
• Magnetoencephalography (MEG): MEG measures magnetic fields produced by neural activity. While it provides excellent temporal resolution and is less susceptible to noise than EEG, it requires large, expensive equipment and is not as widely available.

Exploring Invasive BCIs: Invasive BCIs, on the other hand, involve implanting electrodes directly into the brain. These systems offer much clearer and more precise signals than non-invasive BCIs but come with significant risks, such as infection or damage to brain tissue.

• ECoG (Electrocorticography): ECoG involves placing electrodes directly on the surface of the brain, beneath the skull. This technique provides better signal quality and spatial resolution compared to EEG, making it suitable for clinical applications, particularly in epilepsy treatment. However, it is more invasive, requiring surgical intervention.
• Intracortical Implants: These implants involve inserting electrodes directly into the brain tissue. They provide the highest signal quality and spatial resolution, making them ideal for controlling advanced prosthetic devices or restoring motor function in paralyzed individuals. However, the risks associated with intracortical implants are significant, including potential brain damage and long-term biocompatibility issues.

Wolpaw et al., IEEE Trans Neural Sys Rehab Eng, 2006

Comparative Analysis: Non-Invasive vs. Invasive BCIs:

• Signal Quality: Invasive BCIs offer superior signal quality and spatial resolution due to the direct access to brain tissue. Non-invasive methods, while safer, tend to have lower signal clarity.
• Safety and Risk: Non-invasive BCIs are much safer, as they do not involve surgery. In contrast, invasive BCIs carry risks such as infection, brain tissue damage, and the potential for adverse immune responses.
• Applications: Non-invasive BCIs are suitable for everyday applications, such as gaming, neurofeedback, and basic communication devices. Invasive BCIs are more appropriate for clinical applications where high precision is necessary, such as advanced neuroprosthetics or deep brain stimulation.

Conclusion: Both non-invasive and invasive BCIs have their unique advantages and challenges. While non-invasive BCIs are more accessible and pose fewer risks, invasive BCIs offer superior performance for more demanding applications. As technology progresses, hybrid approaches that combine the benefits of both methods may emerge, offering high-quality signals with minimal risk.

Next Blog: The Technical Backbone of BCIs: Signal Processing and Machine Learning

External References (Reading Recommendation):

• Lebedev, M. A., & Nicolelis, M. A. L. (2006). “Brain-machine interfaces: past, present and future.” Trends in Neurosciences, 29(9), 536–546.
• Schalk, G., & Leuthardt, E. C. (2011). “Brain-computer interfaces using electrocorticographic signals.” IEEE Reviews in Biomedical Engineering, 4, 140–154.

Thank You: I have learned this information from my course EN.585.783 Introduction to Brain-Computer Interface at Johns Hopkins University. A big thanks to my instructors for making this journey enlightening!