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UCSF Team Develops Neuroprosthesis For Decoding Speech In a Paralyzed Patient
Brain-computer interface technology has been progressing rapidly over the recent years in an effort to restore speech, movement and other daily activities in people paralyzed by stroke, disease or physical injury by relying solely on their own brain signals.
A “neuroprosthesis” developed by team of researchers from the University of California San Francisco may be perhaps the closest yet to restoring natural communication patterns. The “neuroprosthesis” that, once trained on an individual patient’s brain patterns, can eventually translate their thoughts into speech.
A study of their system found it was able to decode entire sentences at once with a median rate of about 15 words per minute and an average 75% accuracy, with a word error rate of just over 25%.
In order achieve to such accuracy, the researchers spent over a year-and-a-half training the system to recognize the brain signals of a single paralyzed patient, a man who lost his ability to communicate and much of his movement following a stroke in his brain stem more than 15 years ago.
The patient was tasked with repeatedly attempting to say 50 words on a list of basic terms like water and family after a high-density electrode array was implanted over his speech motor cortex. The attempts took place over the course of 48 sessions. The recoridngs of the training sessions were analyzed by Deep learning algorithms to pick up on patterns in the patient’s brain signals for each word.
The resulting neural network was combined with a natural-language model able to predict probable next words in a sentence allowing the system to more quickly and accurately produce entire sentences at a time, differentiating this system from most other systems that only allow nonverbal patients to communicate on a letter-by-letter basis.
The core difference between the two methods stems from the neuroprosthesis’ ability to pick up on brain signals sent to the vocal tract, rather than those sent to the arm or hand to type out a word or control a cursor.
Moving forward, the researchers plan on expanding the study to include other severely paralyzed participants. They’re also hoping to expand the vocabulary list and up the system’s word-per-minute rate.