{"id":2927,"date":"2023-09-25T09:34:45","date_gmt":"2023-09-25T08:34:45","guid":{"rendered":"https:\/\/springerhealthcare.nl\/france\/?p=2927"},"modified":"2023-09-28T13:14:41","modified_gmt":"2023-09-28T12:14:41","slug":"brain-reading-devices-allow-paralysed-people-to-talk-using-their-thoughts","status":"publish","type":"post","link":"https:\/\/springerhealthplus.nl\/shmigrate\/brain-reading-devices-allow-paralysed-people-to-talk-using-their-thoughts\/","title":{"rendered":"Brain-reading devices allow paralysed people to talk using their thoughts"},"content":{"rendered":"

[vc_row full_width=”stretch_row” content_placement=”middle” css=”.vc_custom_1663187695627{padding-bottom: 37px !important;background-image: url(http:\/\/springerhealthcare.nl\/wp-content\/uploads\/sites\/24\/2022\/09\/science_connect_back.png?id=2486) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}” el_class=”sciencetitle”][vc_column][vc_custom_heading text=”Science Connect” font_container=”tag:h2|text_align:left|color:%23ffffff” use_theme_fonts=”yes” link=”url:https%3A%2F%2Fspringerhealthcare.nl%2Ffrance%2Fscience-connect-september-2023%2F”][vc_column_text css=”.vc_custom_1663186747780{margin-bottom: 40px !important;}”]A Springer Healthcare Initiative For Pharma Professionals<\/span>[\/vc_column_text][vc_column_text]SEPTEMBER 2023
\n<\/span>[\/vc_column_text][\/vc_column][\/vc_row][vc_row css=”.vc_custom_1538651642953{padding-top: 75px !important;}”][vc_column width=”3\/4″ css=”.vc_custom_1538657061395{border-right-width: 1px !important;padding-top: 0px !important;border-right-color: #8faec1 !important;border-right-style: solid !important;}”][vc_custom_heading source=”post_title” font_container=”tag:h1|font_size:40px|text_align:left|line_height:60px” google_fonts=”font_family:Merriweather%3Aregular%2Citalic|font_style:700%20regular%3A700%3Anormal” el_class=”title” css=”.vc_custom_1664358136969{margin-bottom: 30px !important;}”][vc_column_text css=”.vc_custom_1686833173052{padding-bottom: 0px !important;}”]Two studies report considerable improvements in technologies designed to help people with facial paralysis to communicate.<\/strong><\/span>[\/vc_column_text][vc_column_text el_class=”intro-box”]By Miryam Naddaf<\/span>[\/vc_column_text][vc_single_image image=”2932″ img_size=”full” el_class=”hero-img”][vc_column_text css=”.vc_custom_1695903103200{margin-bottom: 30px !important;}” el_class=”sc-img-caption”]A brain-computer interface translates the study participant\u2019s brain signals into the speech and facial movements of an animated avatar. Credit: Noah Berger[\/vc_column_text][vc_column_text]

Brain-reading implants enhanced using artificial intelligence (AI) have enabled two people with paralysis to communicate with unprecedented accuracy and speed.<\/p>\n

In separate studies, both published on 23 August in\u00a0Nature<\/em>1<\/sup>,<\/sup>2<\/sup>, two teams of researchers describe brain\u2013computer interfaces (BCIs) that translate neural signals into text or words spoken by a synthetic voice. The BCIs can decode speech at 62 words per minute and 78 words per minute, respectively. Natural conversation happens at around 160 words per minute, but the new technologies are both faster than any previous attempts.<\/p>\n

\u201cIt is now possible to imagine a future where we can restore fluid conversation to someone with paralysis, enabling them to freely say whatever they want to say with an accuracy high enough to be understood reliably,\u201d said Francis Willett, a neuroscientist at Stanford University in California who co-authored one of the papers1<\/sup>, in a press conference on 22 August.<\/p>\n

These devices \u201ccould be products in the very near future\u201d, says Christian Herff, a computational neuroscientist at Maastricht University, the Netherlands.<\/p>\n

Electrodes and algorithms<\/h2>\n

Willett and his colleagues developed a BCI to interpret neural activity at the cellular level and translate it into text. They worked with 67-year-old Pat Bennett, who has motor neuron disease, also known as amyotrophic lateral sclerosis \u2014 a condition that causes a progressive loss of muscle control, resulting in difficulties moving and speaking.<\/p>\n

First, the researchers operated on Bennett to insert arrays of small silicon electrodes into parts of the brain that are involved in speech, a few millimetres beneath the surface. Then they trained deep-learning algorithms to recognize the unique signals in Bennett\u2019s brain when she attempted to speak various phrases using a large vocabulary set of 125,000 words and a small vocabulary set of 50 words. The AI decodes words from phonemes \u2014 the subunits of speech that form spoken words. For the 50-word vocabulary, the BCI worked 3.4 times faster than an earlier BCI developed by the same team3<\/a><\/sup>\u00a0and achieved a 9.1% word-error rate. The error rate rose to 23.8% for the 125,000-word vocabulary. \u201cAbout three in every four words are deciphered correctly,\u201d Willett told the press conference.<\/p>\n

\u201cFor those who are nonverbal, this means they can stay connected to the bigger world, perhaps continue to work, maintain friends and family relationships,\u201d said Bennett in a statement to reporters.<\/p>\n

Reading brain activity<\/h2>\n

In a separate study2<\/sup>, Edward Chang, a neurosurgeon at the University of California, San Francisco, and his colleagues worked with a 47-year-old woman named Ann, who lost her ability to speak after a brainstem stroke 18 years ago.<\/p>\n

They used a different approach from that of Willett\u2019s team, placing a paper-thin rectangle containing 253 electrodes on the surface on the brain\u2019s cortex. The technique, called electrocorticography (ECoG), is considered less invasive and can record the combined activity of thousands of neurons at the same time. The team trained AI algorithms to recognize patterns in Ann\u2019s brain activity associated with her attempts to speak 249 sentences using a 1,024-word vocabulary. The device produced 78 words per minute with a median word-error rate of 25.5%.<\/p>\n

Although the implants used by Willett\u2019s team, which capture neural activity more precisely, outperformed this on larger vocabularies, it is \u201cnice to see that with ECoG, it’s possible to achieve low word-error rate\u201d, says Blaise Yvert, a neurotechnology researcher at the Grenoble Institute of Neuroscience in France.<\/p>\n

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<\/div>\n
Credit: Chang Lab<\/span><\/div>\n
<\/div>\n<\/div>\n

 <\/p>\n

Chang and his team also created customized algorithms to convert Ann\u2019s brain signals into a synthetic voice and an animated avatar that mimics facial expressions. They personalized the voice to sound like Ann\u2019s before her injury, by training it on recordings from her wedding video.<\/p>\n

\u201cThe simple fact of hearing a voice similar to your own is emotional,\u201d Ann told the researchers in a feedback session after the study. \u201cWhen I had the ability to talk for myself was huge!\u201d<\/p>\n

\u201cVoice is a really important part of our identity. It\u2019s not just about communication, it\u2019s also about who we are,\u201d says Chang.<\/p>\n

Clinical applications<\/h2>\n

Many improvements are needed before the BCIs can be made available for clinical use. \u201cThe ideal scenario is for the connection to be cordless,\u201d Ann told researchers. A BCI that was suitable for everyday use would have to be made of a fully implantable system with no visible connectors or cables, adds Yvert. Both teams hope to continue increasing the speed and accuracy of their devices with more-robust decoding algorithms.<\/p>\n

Furthermore, the participants of both studies still have the ability to engage their facial muscles when thinking about speaking, and their speech-related brain regions are intact, says Herff. \u201cThis will not be the case for every patient.\u201d<\/p>\n

\u201cWe see this as a proof of concept and just providing motivation for industry people in this space to translate it into a product somebody can actually use,\u201d says Willett.<\/p>\n

The devices must also be tested on many more people to prove their reliability. \u201cNo matter how elegant and technically sophisticated these data are, we have to understand them in context, in a very measured way,\u201d says Judy Illes, a neuroethics researcher at the University of British Columbia in Vancouver, Canada. \u201cWe have to be careful with over-promising wide generalizability to large populations,\u201d she adds. \u201cI\u2019m not sure we\u2019re there yet.\u201d<\/p>\n

Nature<\/em>\u00a0620<\/strong>, 930-931 (2023)<\/p>\n

doi: https:\/\/doi.org\/10.1038\/d41586-023-02682-7<\/em><\/p>\n

UPDATES & CORRECTIONS<\/h3>\n

Correction 24 August 2023<\/strong>: An earlier version of this article stated that the BCI developed by Willett and colleagues worked 2.7 times faster than a previous version. This figure has now been corrected.<\/p>\n

References<\/h2>\n
    \n
  1. \n
      \n
    1. Willett, F. R. et al. Nature<\/em> https:\/\/doi.org\/10.1038\/s41586-023-06377-x<\/a> (2023).<\/li>\n
    2. Metzger, S. L. et al. Nature<\/em> https:\/\/doi.org\/10.1038\/s41586-023-06443-4<\/a> (2023).<\/li>\n
    3. Willett, F.R. et al. Nature<\/em> 593<\/strong>, 249\u2013254 (2021).<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n[\/vc_column_text][vc_column_text]\u00a9 2023 Springer Nature Limited.[\/vc_column_text][\/vc_column][vc_column width=”1\/4″ css=”.vc_custom_1538657003956{padding-top: 0px !important;padding-bottom: 0px !important;}”][vc_custom_heading text=”ISSUES” font_container=”tag:h3|font_size:12px|text_align:left|color:%23333333|line_height:18px” use_theme_fonts=”yes” el_class=”titlewidget”][vc_column_text el_class=”columnflex” css=”.vc_custom_1664404002021{margin-bottom: 40px !important;}”]

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