The human brain is one of the most complex structures in the known universe, with billions of neurons and trillions of synapses, all working in synchrony to orchestrate the regulation of bodily functions.
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Studying the brain and deciphering its functions have always been one of the primary goals of neuroscience. One of its most recent and promising technological developments is the Brain-Computer Interface (BCI). A BCI is a direct communication pathway between the brain and an external device, bypassing the traditional neuromuscular system.
BCIs convert brain activity into signals that can be processed and interpreted by a computer, which can then be implemented to control an external device or provide feedback to the user.
Types of Brain-Computer Interfaces
There are several types of BCIs, and they can be categorized based on their level of invasiveness: invasive, semi-invasive, and non-invasive.
Invasive BCIs involve the direct implantation of electrodes into the brain, either on the surface of the cortex or deep within the brain tissue. In this situation, the implanted electrodes are known as “depth electrodes”, given how deep it penetrates the brain tissue. This method provides high spatial and temporal resolution, allowing for precise recordings of neural activity; however, it carries risks such as infection, hemorrhage, and brain damage.
Semi-invasive BCIs carry the same risks as invasive BCIs, although to a lesser degree, whereby the electrodes are placed upon the layers just above the cortex, such as the dura and arachnoid, therefore not penetrating as deep as the invasive BCI technology. However, the accuracy and reliability of semi-invasive BCIs may be constrained due to their inability to capture neural signals as accurately as invasive BCIs. An example semi-invasive BCI technology is electrocorticography (ECoG).
Non-invasive BCIs use sensors placed on the scalp, such as electroencephalography (EEG) or magnetoencephalography (MEG), to record neural activity. While non-invasive BCIs are safer than the aforementioned methods, they have lower spatial resolution and are prone to interference from external sources such as muscle activity and environmental noise.
Advancements in Brain-Computer Interfaces
In recent years there have been significant advancements in the development of BCIs, especially in combination with artificial intelligence (AI), particularly in the areas of signal processing, machine learning, and robotics.
Signal processing techniques, such as filtering and artifact removal, have improved the quality of neural recordings, allowing for a more accurate interpretation of neural activity. Machine learning algorithms, such as deep neural networks, have improved the accuracy of BCI systems by enabling them to learn and adapt to individual users' neural activity patterns. Robotics and prosthetics have also seen significant improvements, with the development of more sophisticated and responsive devices that BCIs can control.
One of the most significant advancements in BCIs is the development of hybrid BCIs, which combines different input and output methods to improve the overall performance of the system. For example, a hybrid BCI could combine invasive recording methods with non-invasive output methods to better control an external device while minimizing the risks associated with invasive procedures.
Applications of Brain-Computer Interfaces
The potential applications of BCIs are numerous and diverse, ranging from medical to commercial and entertainment. Here are just a few examples:
1. Medical Applications:
Brain-Computer Interfaces (BCIs) have emerged as a promising avenue in the field of medicine, particularly for individuals suffering from paralysis or severe motor disabilities. These innovative technologies enable users to control prosthetic limbs, wheelchairs, or their immediate environment, significantly enhancing their independence and overall quality of life.
Additionally, BCIs have demonstrated the potential to substitute lost motor function, such as hand or arm movements, by utilizing neural signals to control a robotic limb. Moreover, they have been utilized to control exoskeletons, which can assist individuals with lower limb paralysis to walk again. BCIs have the potential to revolutionize the field of medical technology, offering novel solutions to long-standing problems and drastically improving the lives of those affected by physical disabilities.
2. Entertainment and Gaming:
Brain-Computer Interfaces (BCIs) have increasingly gained attention as a novel tool for entertainment and gaming. For example, electronic games can now be controlled using brain signals, providing a unique and immersive experience for the user. Moreover, BCIs have been seamlessly integrated into virtual reality (VR) systems to enhance the gaming experience.
This is accomplished by detecting the user's emotions and translating them into in-game actions, thereby heightening the sense of engagement and immersion. BCIs could create highly personalized and immersive experiences, such as controlling the ambient lighting, sound, or temperature of a room, thereby revolutionizing the entertainment industry.
3. Communication:
BCIs have demonstrated the potential to aid individuals afflicted with communication disabilities, such as locked-in syndrome or amyotrophic lateral sclerosis (ALS). Such conditions impose a significant challenge for verbal or traditional communication, thereby reducing the quality of life of those affected. Through the translation of neural activity into speech or text, BCIs provide a novel means of communication for individuals with such disabilities, thus offering the potential to significantly enhance their quality of life.
4. Education:
Brain-Computer Interfaces (BCIs) have also proven to be useful for educational purposes, providing valuable insights for researchers and educators into the inner workings of the human brain, as well as the process of learning. BCIs offer a unique opportunity to investigate brain plasticity, which refers to the brain's remarkable capacity to adjust and adapt in response to new experiences and information. This knowledge can be used to develop innovative teaching approaches that cater to a diverse range of learning styles, ultimately enhancing the effectiveness of education.
5. Military and Defence:
BCIs have also been researched for military and defense applications. BCIs have been utilized to control unmanned vehicles, such as drones or robots, through the operator's thoughts. Through this technological leap, it reduces the risk of human casualties in dangerous situations, whereby BCIs are also able to enhance the cognitive abilities of soldiers, such as improving reaction times or decision-making skills.
In summary, BCIs present an opportunity to revolutionize the way in which we engage with technology and our environment. The scope of BCI applications is extensive, spanning medical, communication, and cognitive domains. As research and development progress, BCIs have the potential to transform multiple industries, ultimately contributing to an improved quality of life for individuals with disabilities and providing novel avenues for exploring and interacting with our surroundings.
References and Further Reading
Introduction to Brain-Computer Interfaces (2018) [Online]. NeurotechEDU; [cited 2023 May 12]. Available at: http://learn.neurotechedu.com/introtobci/
Klaes C. (2018) Chapter 28 - Invasive Brain-Computer Interfaces and Neural Recordings From Humans. Handbook of Behavioral Neuroscience. p. 527-539. https://www.sciencedirect.com/science/article/abs/pii/B9780128120286000288
Rao R. (2013) Brain-Computer Interfacing: An Introduction. [Online] Cambridge: Cambridge University Press, p. 149-176. Available at: https://assets.cambridge.org/97805217/69419/frontmatter/9780521769419_frontmatter.pdf
Jiang X., Bian G.B., Tian Z. (2019) Removal of Artifacts from EEG Signals: A Review. Sensors (Basel). 19(5), p. 987. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427454/
Janapati R., Dalal V., Sengupta R. (2023) Advances in modern EEG-BCI signal processing: A review. Materials Today: Proceedings. 80(Part 3), pp. 2563-2566. https://www.sciencedirect.com/science/article/abs/pii/S2214785321048033
Ahn M., Jun S.C., Yeom H.G., Cho H. (2022) Editorial: Deep Learning in Brain-Computer Interface. Front Hum Neurosci. 16. https://www.frontiersin.org/articles/10.3389/fnhum.2022.927567/full
Baniqued P.D.E., Stanyer E.C., Awais M., et al. (2022) Brain–computer interface robotics for hand rehabilitation after stroke: a systematic review. J NeuroEngineering Rehabil. 18(1), p. 15. https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-021-00820-8
Li Z., Zhang S., Pan J. (2019) Advances in Hybrid Brain-Computer Interfaces: Principles, Design, and Applications. Comput Intell Neurosci. 2019, p. 3807670. https://pubmed.ncbi.nlm.nih.gov/31687006/
Li X., Chen J., Shi N., Yang C., Gao P., Chen X., Wang Y., Gao S., Gao X. (2023) A hybrid steady-state visual evoked response-based brain-computer interface with MEG and EEG. Expert Systems with Applications. 223, p. 119736. https://www.sciencedirect.com/science/article/pii/S0957417423002373
Spüler M, Krumpe T, Walter C, Scharinger C, Rosenstiel W, Gerjets P. (2017) Brain-Computer Interfaces for Educational Applications. Informational Environments: Effects of use, Effective Designs. Springer; pp. 177-201. https://link.springer.com/chapter/10.1007/978-3-319-64274-1_8
Mridha M.F., Das S.C., Kabir M.M., Lima A.A., Islam M.R., Watanobe Y. (2021) Brain-Computer Interface: Advancement and Challenges. Sensors (Basel). 21(17), p. 5746. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8433803/
Binnendijk A, Marler T, Bartels E.M. (2023) Brain-Computer Interfaces: U.S. Military Applications and Implications, An Initial Assessment. [Online] Santa Monica, CA: RAND Corporation; Available at: https://www.rand.org/pubs/research_reports/RR2996.html