Imagine a world where thoughts translate into words without uttering a single sound via brain implants.
At Duke University, a groundbreaking project involving neuroscientists, neurosurgeons, and engineers, has birthed a speech prosthetic capable of converting brain signals into spoken words. This innovation, detailed in the journal Nature Communications, could redefine communication for those with speech-impairing neurological disorders.
Currently, people with conditions like ALS or locked-in syndrome rely on slow and cumbersome communication methods. Typically, speech decoding rates hover around 78 words per minute, while natural speech flows at about 150 words per minute. This gap in communication speed underscores the need for more advanced solutions.
To bridge this gap, Duke’s team, including neurologist Gregory Cogan and biomedical engineer Jonathan Viventi, has introduced a high-tech approach. They created an implant with 256 tiny sensors on a flexible, medical-grade material. Capturing nuanced brain activities essential for speech, this device marks a significant leap from previous models with fewer sensors.
The Test Drive: From Lab to Real Life
The real challenge was testing the implant in a real-world setting. Patients undergoing unrelated brain surgeries, like Parkinson’s disease treatment or tumor removal, volunteered to test the implant. The Duke team, likened to a NASCAR pit crew by Dr. Cogan, had a narrow window of 15 minutes during these surgeries to conduct their tests.
Patients participated in a simple task: listening to and repeating nonsensical words. The implant recorded their brain’s speech-motor cortex activities, coordinating muscles involved in speech. This data is then fed into a machine learning algorithm, managed by Suseendrakumar Duraivel, to predict the intended sounds based on brain activity.
While accuracy varied, some sounds and words were correctly identified up to 84% of the time. Despite the challenges, such as distinguishing between similar sounds, the results were promising, especially considering the brevity of the data collection period.