Summary: Using ECoG and machine learning, researchers decoded spoken words and phrases in real-time from brain signals that control speech. The technology could eventually be used to help those who have lost vocal control to regain their voice.
UC San Francisco scientists recently showed that brain activity recorded as research participants spoke could be used to create remarkably realistic synthetic versions of that speech, suggesting hope that one day such brain recordings could be used to restore voices to people who have lost the ability to speak. However, it took the researchers weeks or months to translate brain activity into speech, a far cry from the instant results that would be needed for such a technology to be clinically useful.
Now, in a complementary new study, again working with volunteer study subjects, the scientists have for the first time decoded spoken words and phrases in real time from the brain signals that control speech, aided by a novel approach that involves identifying the context in which participants were speaking.
“For years, my lab was mainly interested in fundamental questions about how brain circuits interpret and produce speech,” said speech neuroscientist Eddie Chang, MD, a professor of neurosurgery, Bowes Biomedical Investigator, and member of the Weill Institute for Neurosciences at UCSF. “With the advances we’ve seen in the field over the past decade it became clear that we might be able to leverage these discoveries to help patients with speech loss, which is one of the most devastating consequences of neurological damage.”
Patients who experience facial paralysis due to brainstem stroke, spinal cord injury, neurodegenerative disease, or other conditions may partially or completely lose their ability to speak. However, the brain regions that normally control the muscles of the jaw, lips, tongue, and larynx to produce speech are often intact and remain active in these patients, suggesting it could be possible to use these intentional speech signals to decode what patients are trying to say.
“Currently, patients with speech loss due to paralysis are limited to spelling words out very slowly using residual eye movements or muscle twitches to control a computer interface,” Chang explained. “But in many cases, information needed to produce fluent speech is still there in their brains. We just need the technology to allow them to express it.”
Context Improves Real-Time Speech Decoding
As a stepping stone towards such a technology, Chang’s lab has spent years studying the brain activity that controls speech with the help of volunteer research participants at the UCSF Epilepsy Center.
These patients – all of whom have normal speech – have had a small patch of tiny recording electrodes temporarily placed on the surface of their brains for a week or more to map the origins of their seizures in preparation for neurosurgery. This involves a technique called electrocorticography (ECoG), which provides much richer and more detailed data about brain activity than is possible with non-invasive technologies like EEG or fMRI. While they are in the hospital, some of these patients agree to let Chang’s group use the already-implanted ECoG electrodes as part of research experiments not directly related to their illness.
In the new study, published July 30 in Nature Communications, researchers from the Chang lab led by postdoctoral researcher David Moses, PhD, worked with three such research volunteers to develop a way to instantly identify the volunteers’ spoken responses to a set of standard questions based solely on their brain activity, representing a first for the field.
To achieve this result, Moses and colleagues developed a set of machine learning algorithms equipped with refined phonological speech models, which were capable of learning to decode specific speech sounds from participants’ brain activity. Brain data was recorded while volunteers listened to a set of nine simple questions (e.g. “How is your room currently?”, “From 0 to 10, how comfortable are you?”, or “When do you want me to check back on you?”) and responded out loud with one of 24 answer choices. After some training, the machine learning algorithms learned to detect when participants were hearing a new question or beginning to respond, and to identify which of the two dozen standard responses the participant was giving with up to 61 percent accuracy as soon as they had finished speaking.
“Real-time processing of brain activity has been used to decode simple speech sounds, but this is the first time this approach has been used to identify spoken words and phrases,” Moses said. “It’s important to keep in mind that we achieved this using a very limited vocabulary, but in future studies we hope to increase the flexibility as well as the accuracy of what we can translate from brain activity.”
One of the study’s key findings is that incorporating the context in which participants were speaking dramatically improved the algorithm’s speed and accuracy. Using volunteers’ brain activity to first identify which of the predefined questions they had heard – which the algorithm did with up to 75 percent accuracy – made it possible to significantly narrow down the range of likely answers, since each answer was only an appropriate response to certain questions.
“Most previous approaches have focused on decoding speech alone, but here we show the value of decoding both sides of a conversation – both the questions someone hears and what they say in response,” Chang said.
“This reinforces our intuition that speech is not something that occurs in a vacuum and that any attempt to decode what patients with speech impairments are trying to say will be improved by taking into account the full context in which they are trying to communicate.”
First Attempt to Restore Speech in Clinic
Following a decade of advances in understanding the brain activity that normally controls speech, Chang’s group has recently set out to discover whether these advances could be used to restore communication abilities to paralyzed patients.
In collaboration with colleague Karunesh Ganguly, MD, PhD, an associate professor of neurology at UCSF, Chang’s lab has launched a study known as “BRAVO” (BCI Restoration of Arm and Voice), to determine whether ECoG neural interface implants like those used in Moses’s study can be used to restore a variety of movement and communication abilities to patients with paralysis caused by stroke, neurodegenerative disease, or traumatic brain injury.
Previous studies have enabled paralyzed individuals to control a robotic arm or computer cursor via arrays of sharp electrodes that are physically inserted into areas of the brain controlling movement. In contrast, ECoG electrodes sit gently on the surface of the brain without penetrating the tissue, and have the potential to be a better option for long-term BCIs. Such neural interfaces have already been used to monitor seizure activity in patients with epilepsy for many years without adverse side effects.
The team has recently enrolled one research participant with significant movement and speech disabilities in the BRAVO study, but the project at too early a stage to report any results.
The researchers emphasize that it is still unclear whether the approaches the lab is currently using to decode the brain activity of research subjects with intact speech – by training a computer-based on examples of their voices – can also work for people who are unable to speak. Instead, study participants may have to learn to use an implanted speech prosthesis over time based on ongoing feedback from their initial performance – a process that will make the kind of real-time speech decoding demonstrated by Moses’s new study even more critical.
Moses’s new study was funded by through a multi-institution sponsored academic research agreement with Facebook Reality Labs (FRL), a research division within Facebook focused on developing augmented- and virtual-reality technologies. As FRL has described, the goal for their collaboration with the Chang lab, called Project Steno, is to assess the feasibility of developing a non-invasive, wearable BCI device that could allow people to type by imagining themselves talking.
The final phase of Project Steno will provide funding and engineering support over the next year for the lab’s efforts to enable a single research participant with speech loss to generate text on a computer screen, in coordination with the lab’s broader BRAVO study. All research participant data are collected by UCSF and maintained as confidential on UCSF-owned computing systems, and are not shared with third parties, including FRL. A limited number of researchers on Facebook’s BCI team are working directly with Chang lab researchers to provide input on this project and have limited access to de-identified data onsite at UCSF.
Funding: The BRAVO study is supported by seed funding from philanthropic foundations, and the researchers are seeking additional support from the National Institutes of Health (NIH). NEUROTECHNOLOGY ETHICS
When scientists talk about designing technology to decode the brain activity underlying speech, it is easy to think they are talking about reading people’s minds, with all the serious ethical concerns that would imply. In reality any sinister attempt to intrude on a person’s inner thoughts is virtually impossible, while decoding what they are trying to say out loud – a clinically urgent need for people with paralysis – is merely very hard.
That said, now is the perfect time to ensure ethical concerns are integrated into the design of new brain technologies from the ground up, which is why UCSF’s Winston Chiong and Eddie Chang are leading an NIH-funded neuroethics initiative to address just these questions.
About this neuroscience research article
Source: UCSF Media Contacts: Nicholas Weiler – UCSF Image Source: The image is credited to Noah Berger.
Real-time decoding of question-and-answer speech dialogue using human cortical activity
Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance’s identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate.