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Mind-Reading Device Translates Thoughts into Speech in Breakthrough for Paralysis Patients
In a significant stride for assistive technology, scientists have engineered a novel device capable of decoding brain activity and converting thoughts into audible speech. This innovative brain-computer interface (BCI) system, developed by researchers at the University of California, Berkeley, represents a potential game-changer for individuals with paralysis and speech impairments, offering a new pathway for communication.
Brain-Computer Interface Converts Brainwaves to Spoken Words
The groundbreaking system employs electrodes applied to the scalp to monitor brain activity and brainwaves. These signals are then processed and translated into comprehensible speech by a computer. The artificial intelligence-powered system converts the analyzed brainwaves into spoken words, read aloud by a synthetic voice.
Researchers are optimistic that this technology could restore communicative abilities for paralyzed individuals by interpreting brain activity from the motor cortex – the brain region controlling speech – and transforming it into understandable spoken language. Even when the physical ability to speak is lost, the motor cortex continues to generate speech-related signals.
Real-Time Speech Synthesis Achieved
Utilizing sophisticated artificial intelligence (AI) models, the team successfully captures brain signals from the motor cortex and converts them into sound with minimal delay – approximately one second. This rapid processing enables continuous speech output, a marked improvement over earlier systems that experienced significant lag times.
Successful Trial with Paralyzed Participant
The BCI system underwent testing with Ann, a woman with severe paralysis unable to speak. Ann had previously participated in earlier research by the same team, where a prior iteration of their system exhibited an eight-second delay. This new research showcases substantial advancement in speed and efficacy.
Refinement of Decoding Unseen Words
Kaylo Littlejohn, a Ph.D. candidate at UC Berkeley’s Department of Electrical Engineering and co-lead author of the study, explained the research goals: ‘We aimed to determine if our technology could generalize to previously unseen words and effectively decode Ann’s natural speech patterns.’
The brain-computer interface (BCI) breakthrough enables near-real-time speech streaming with no delay, using electrodes on the skull to capture brain activity from the motor cortex. It was tested on a woman named Ann [pictured] who is paralyzed and cannot speak
Littlejohn added, ‘Our findings indicate that the model performs effectively in this regard, suggesting it is genuinely learning the foundational elements of sound and voice.’
Patient Testimonial: Enhanced Connection and Control
Ann, who experienced a stroke in 2005 resulting in paralysis, conveyed to researchers that the device empowered her communication and fostered a stronger sense of physical connection.
Advancements in Brain-Wave Decoding Technology
Technology capable of translating brainwaves into spoken sentences remains in early stages of development. Previous research demonstrated limited success, primarily decoding individual words rather than complex phrases or complete sentences.
However, the California-based research team believes this new proof-of-concept study, published in Nature Neuroscience, will accelerate progress in the field.
Mapping Brain Regions for Speech
Several regions of the brain are involved in speech production, including the motor cortex, where specific words possess unique ‘fingerprints.’ Within this area, distinct brainwave signatures are generated for each sound, facilitating the identification of various words, like ‘hello’ and ‘goodbye.’
The process of speaking involves the brain sending signals to the lips, tongue, and vocal cords, alongside adjustments in breathing patterns.
AI-Powered Speech Synthesizer
Researchers trained a ‘naturalistic speech synthesizer’ AI using Ann’s brainwaves. The AI was designed to analyze, interpret, and convert these brain signals into spoken words.
Electrodes were positioned on Ann’s scalp to capture motor cortex activity as she attempted to articulate simple phrases such as ‘Hey, how you?’
As Ann formed the thoughts in her head and tried to speak them, her motor cortex generated signals captured by the electrodes. Researchers incorporated audio of her voice from before she became paralyzed, and AI used a text-to-speech model to generate simulated audio in her natural voice
As Ann formulated sentences mentally, her motor cortex emitted speech commands, even without physical articulation. These signals were captured by the electrodes.
Researchers segmented the signals into brief time intervals representing different parts of the sentences.
By integrating pre-stroke audio recordings of Ann’s voice, the AI employed a text-to-speech model to generate simulated audio in her own voice.
Ann was presented with text prompts like, ‘Hello, how are you?’ and mentally practiced speaking them. This mental rehearsal activated her motor cortex as if she were physically speaking, despite the absence of sound.
The AI system progressively learned her specific speech patterns, enabling her to articulate words she hadn’t explicitly trained the system for, such as ‘Alpha’ and ‘Bravo.’
The system also began to recognize words not explicitly visualized, intelligently filling in gaps to construct complete sentences.
Dr. Gopala Anumanchipalli, an electrical engineer at UC Berkeley and study co-leader, noted, ‘We observe that relative to the intended signal, the initial sound is produced within one second.’
‘Furthermore,’ Dr. Anumanchipalli added, ‘the device facilitates continuous speech decoding, allowing Ann to speak without interruption.’
According to Dr. Littlejohn, the system’s high accuracy represents a substantial advance: ‘Previously, it was uncertain whether intelligible speech could be streamed from brain activity in real time.’
The AI gradually learned her speech patterns, allowing her to speak words she hadn’t been trained to visualize. It also began recognizing words she hadn’t consciously thought of, filling in gaps to create full sentences
Growing Interest in BCI Technology
Brain-computer interface (BCI) technology is experiencing increased interest from scientists and technology companies alike.
In 2023, researchers at Brown University’s BrainGate consortium successfully implanted sensors into the cerebral cortex of Pat Bennett, diagnosed with ALS.
Through 25 training sessions, an AI algorithm learned to decode electrical signals from Ms. Bennett’s brain, identifying phonemes – fundamental speech sounds like ‘sh’ and ‘th’ – based on neural activity patterns.
The decoded brainwaves were then processed by a language model, which assembled them into words and displayed her intended speech on a screen.
When limited to a 50-word vocabulary, the error rate was approximately nine percent. However, expanding the vocabulary to 125,000 words, encompassing nearly every word a person might use, increased the error rate to 23 percent, researchers found.
While the study results did not specify the exact word count upon training completion, researchers have confirmed the capacity of machine-learning tools to recognize thousands of words.
Although results are not yet flawless, researchers believe their findings signify a considerable step forward in perfecting brain wave-to-speech technologies.
Neuralink’s Human Trials
Concurrently, Elon Musk’s Neuralink company implanted its BCI device in 29-year-old Noland Arbaugh in January 2024, marking him as the first human participant in Neuralink’s clinical trials.
Elon Musk’s Neuralink was implanted in 29-year-old Noland Arbaugh’s head in January 2024, making him the first human participant in Neuralink’s clinical trial
Neuralink’s Brain-Computer Interface (BCI) allows for direct communication between the brain and external devices, like a computer or smartphone
Arbaugh sustained severe brain trauma in 2016, resulting in paralysis from the shoulders down.
He was selected to participate in the clinical trial of the Neuralink device, which enables direct communication between the brain and external devices like computers or smartphones.
The Neuralink chip, implanted in Arbaugh’s brain, is connected to over 1,000 electrodes positioned in the motor cortex.
When neurons fire, indicating intended movements such as hand motions, the electrodes capture these signals. The data is wirelessly transmitted to an application, enabling Arbaugh to operate devices with his thoughts.
Arbaugh likened using Neuralink to adjusting a computer cursor, moving it based on prompts while the system learns his intentions over time.
Five months post-implantation, Arbaugh reports improved quality of life, particularly in texting, where he can now send messages in seconds.
He utilizes a virtual keyboard and customized dictation software, alongside playing chess and Mario Kart through the same cursor control technology.
Future Directions in BCI Speech Technology
The findings from the University of California research team represent a significant advancement, bringing researchers closer to achieving natural speech with BCI devices and paving the way for future progress.
Dr. Littlejohn stated, ‘Ongoing research focuses on assessing the accuracy with which we can decode paralinguistic features from brain activity.’
‘Addressing this long-standing challenge, even in conventional audio synthesis fields, would bridge the divide towards truly naturalistic and complete speech synthesis,’ Dr. Littlejohn concluded.