Bridging Minds and Machines: Brain-Computer Interfaces

Brain-Computer Interface (BCI) is a type of innovative technology that has recently received great recognition in the field of science and technology. BCIs aid communication between the brain and various machines such as computers. Some real-life applications of BCIs include medical applications, such as predicting and detecting seizures in epilepsy patients, improving memory through targeted exercises for patients with neurodegenerative diseases, and collecting data for research on human behavior. Additionally, BCIs are sometimes used for military defense when devices such as drones need to be controlled remotely and in combat scenarios where rapid decision-making is necessary. When the user generates signals with intention, a trained BCI is able to decode those signals and translate them into commands for an output machine or device. Using a BCI eliminates the need for users to have voluntary control of their muscles to interact with devices around them. 

The first applications of BCIs were created with the purpose of aiding people with locked-in syndrome, those who have lost the ability to control their muscles used to communicate. Locked-in syndrome can be a result of long-term neurodegenerative brain diseases, and affected individuals are generally still cognitively aware. Their thoughts and feelings are unaffected, but they have no way of sharing them with the world, which is why BCIs were invented to help these people. However, while original BCIs were designed for locked-in individuals, their values to help with other degrees of physical impairment were quickly recognized. Researchers began to recognize how BCIs could be implemented to replace, restore, or supplement muscle control for people with physical disabilities.

So, how do BCIs operate? Well, there are three steps to the brain-computer interface: collecting signals, interpreting the signals, and outputting commands to a machine based on the signals. The first step is  signal acquisition. Brain-computer interfaces measure brain signals using a particular sensor modality that are then transmitted to a computer. The way signals are collected may differ depending on the type of BCI. For example, non-invasive BCIs place the sensor on the scalp, measuring electrical potentials produced by the brain using electroencephalography (EEG) signals. On the other hand, semi-invasive BCIs place the electrodes on an exposed surface of the brain while invasive BCIs directly place the electrode onto the brain cortex. Most often, non-invasive BCIs are used because they have the least potential risks. 

The next step is feature extraction. The BCI analyzes the signals, distinguishing pertinent signal characteristics, which should be correlated with the intent or need of the user. This step usually represents signals in a compact form, translating the signals for the next step, allowing the signals to be understood by the machine. The algorithm converts the features into commands for the machine or device, frequently a computer, and this should be dynamic in order to accommodate in cases of abrupt changes in the signal features. 

The final step is the device output. Commands from the previous step, regarding the feature transition algorithm, operate on an external machine. This provides functions and operation outputs feedback for the user, therefore closing the control loop. 

Currently, BCIs are changing human interaction with technology, offering new possibilities for healthcare and communication. They are especially important for patients with disabilities, as BCIs provide patients with a way to interact with their environment. However, some ethical challenges remain and scientists and researchers must keep these in mind.

Works Cited

Intro to Brain Computer Interface. (n.d.). NeurotechEDU. https://learn.neurotechedu.com/introtobci/

McFarland, D. J., & Wolpaw, J. R. (2011). Brain-Computer Interfaces for Communication and Control. Communications of the ACM, 54(5), 60–66. https://doi.org/10.1145/1941487.1941506

Shih, J. J., Krusienski, D. J., & Wolpaw, J. R. (2012). Brain-computer interfaces in medicine. Mayo Clinic proceedings, 87(3), 268–279. https://doi.org/10.1016/j.mayocp.2011.12.008

What is BCI? (n.d.). Cumming School of Medicine. https://cumming.ucalgary.ca/research/pediatric-bci/bci-program/what-bci