Brain Computing Interface

By - Sourabh Ghosh

A paralyzed man has become the first man to tweet using only his “direct thought”. Yes, you read it right! Philip O’ Keefe suffers from Amyotrophic Lateral Sclerosis or ALS, that has made him completely paralyzed, unable to move his limbs. But how could such a person be able to tweet without the use of his voice or keystrokes? Well, that was quite simple. He was able to turn his thoughts directly to text using the Stentrode Brain Computer Interface (or BCI). Now the question is, what exactly is this BCI? How did it help Philip to tweet stuffs without any keystrokes or voice?

The Brain Computer Interface (BCI) is the linkage of the brain to computers through scalp, subdural or intracortical electrodes. In simple terms, Brain Computing Interface or BCI primarilys refers to the Neurobionics methodology that enables communication based on the brain signals without any use of peripheral nerves or muscles. In today’s world of advanced technology, BCI has emerged as one of the most efficient methods that are being developed to enable individuals with any sort of motor disorders (for example, amyotrophic lateral sclerosis (ALS), brainstem stroke, cerebral palsy, and spinal cord injury) or neuromuscular diseases (like Motor Neurone Disease (MND), Full Sliding Status (TSD)) etc to communicate with their environment. In the last 15 years, the pace of BCI has grown rapidly, emerging as an exciting advance in neuroscience and engineering.

BCIs are transforming the brain activities taken from the individual into movement or writing through this application. For this purpose, it uses brain signals to visualize brain activities. BCI basically uses EEG signals. After obtaining the brain signals employing a particular sensor modality like electrodes, brain implants, etc, the signals are amplified to levels suitable for electronic processing (and they will even be subjected to filtering to get rid of electrical noise or other undesirable signal characteristics, such as 60-Hz power line interference). The signals are then digitized and transmitted to a computer. Then feature extraction is performed for analysing the digital signals to distinguish pertinent signal characteristics (i.e., signal features related to the person’s intent) from extraneous content and representing them in a compact form suitable for translation into output commands. These features should have strong correlations with the user’s intent. The resulting signal features are then passed to the feature translation algorithm, which converts the features into suitable commands for the output device (i.e., commands that accomplish the user’s intent). Then finally commands from the feature translation algorithm operate the external device, providing functions like letter selection, cursor control, robotic arm operation, and so forth. The device operation provides feedback to the user, thus closing the loop.

Many researchers globally are developing BCI systems that many years ago were within the realm of fantasy. These systems use different brain signals, recording methods, and signal-processing algorithms. Many people nowadays with severe disabilities are already using a BCI for basic communication and control in their daily lives. With better signal-acquisition hardware, clear clinical validation, viable dissemination models, and, probably most important, increased reliability, BCIs may become a significant communication and control technology for people with disabilities—and possibly for the general population also.