Paralyzed Patient Speaks Using Brain-Computer Interface Controlled by Thought

Innovative Study Brings Hope for Communication in Paralyzed Individuals

Researchers at Tel Aviv University and Tel Aviv Sourasky Medical Center have made a significant breakthrough with a brain-computer interface that allows a paralyzed patient to communicate using their thoughts. This pioneering study provides new hope for those who are completely paralyzed to regain the ability to speak through artificial speech.

In this groundbreaking experiment, a participant imagined saying specific syllables. Deep brain electrodes transmitted these thoughts as electrical signals to a computer, which then vocalized them. This achievement not only offers a rare insight into brain function but also paves the way for future technologies that may enable paralyzed individuals to express themselves again.

Study and Experimentation

The study was a collaborative effort involving Dr. Ariel Tankus of Tel Aviv University’s School of Medical and Health Sciences, Dr. Ido Strauss, and the Functional Neurosurgery Unit at Tel Aviv Sourasky Medical Center. They worked with an epileptic patient who required neurosurgical intervention due to medication-resistant epilepsy.

The patient, who already had electrodes implanted for epilepsy treatment, participated in the study while waiting for another seizure, which would help doctors locate the source of his condition. This setup allowed researchers to utilize the existing brain electrodes to conduct their speech neuroprosthesis experiment.

Neuroprosthesis Experiment

With the patient's consent, researchers asked him to repeat the syllables /a/ and /e/ aloud. They recorded the brain activity associated with these syllables and trained machine learning models to identify the specific brain cells involved. Remarkably, they were able to pinpoint the electrical activity that indicated the desire to say /a/ and /e/. The computer then vocalized these imagined syllables based on the electrical patterns.

Although the study is in its ALSearly stages, focusing on just two syllables, it represents a critical step toward achieving complete speech for paralyzed individuals. As Dr. Tankus noted, even the ability to signal "yes" or "no" using two syllables can significantly enhance communication for fully paralyzed persons.

This machine learning technique also has preventative applications. For example, it could be used for ALS patients in the early stages of the disease. By learning their thought and speech patterns while they can still talk, the machine can help them communicate even after they lose the ability to speak physically.

"Our study is a significant step toward developing a brain-computer interface that can replace the brain’s control pathways for speech production," Tankus concluded. "This will allow completely paralyzed individuals to communicate voluntarily with their surroundings once again."