Machine translation of cortical activity to text with an encoder-decoder framework.

Joseph G Makin, David A Moses, Edward F Chang

April 2020 Nat Neurosci

Synopsis of Social media discussions

Across the discussions, several posts commend the paper’s achievement of a 3% word error rate and highlight its significance for brain-machine interfaces, with words like 'impressive', 'next level', and 'revolutionizing' demonstrating enthusiasm and recognition of its transformative potential. The tone and emphasis on technical details and future applications showcase deep engagement and consensus on high impact.

A
Agreement
 Strong agreement

Most discussions express strong support and admiration for the research, emphasizing its impressive accuracy and potential.

I
Interest
 High level of interest

Posts show high curiosity, with many highlighting the technological breakthrough and its implications.

E
Engagement
 High engagement

Many comments analyze the methodology, mention specific error rates, and discuss future applications, reflecting deep involvement.

I
Impact
 High level of impact

The overall tone suggests the study is considered highly impactful in neuroscience, AI, and brain-computer interfaces, potentially revolutionizing the field.

Social Mentions

YouTube

4 Videos

Bluesky

1 Posts

Facebook

9 Posts

Twitter

105 Posts

Blogs

18 Articles

News

83 Articles

Reddit

32 Posts

Metrics

Video Views

959,474

Total Likes

13,210

Extended Reach

2,850,793

Social Features

252

Timeline: Posts about article

Top Social Media Posts

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Decoding Brain Signals to Text Using Encoder-Decoder Neural Networks

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3 views

  • SpeakThru
    @SpeakThru (Twitter)

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  • 심정곤
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    "법으로 규제해야 한다"... 최근 과학자들이 예견하는 AI의 섬뜩한 미래 https://t.co/IBjBGp8MZi via @YouTube 신경생리학적인 건 조금 그렇고 휴먼 베이스로 조금씩 접근 중. 영화보면 헬멧을 씌우고 거짓말 탐지기도 있고.
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    June 5, 2023

  • Mitsuhiro Yoshida
    @mitstek (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework | Nature Neuroscience https://t.co/9RsF3UI3JL
    view full post

    February 6, 2023

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    May 31, 2021

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    April 29, 2021

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  • Mai Tanaka
    @MaiAndResearch (Twitter)

    4. Makin et al. 2020. Machine translation of cortical activity to text with an encoder-decoder framework https://t.co/BNZBYXsgZR Vocabulary pool limited at 250 words, tested on 30-50 sentences. But still, 3% word error rate is impressive. #100papers #論文百本ノック
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    February 19, 2021

  • Mind Reading
    @MindReading_ (Twitter)

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    December 21, 2020

  • The Machine Learning Bot
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    RT @WSISMM: Machine translation of cortical activity to text with an encoder–decoder framework #wsismm #AI https://t.co/jiIo1mpT5k
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    September 13, 2020

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    September 13, 2020

    1

  • Toshihico　Shiracawa
    @candidusflumen (Twitter)

    「Machine translation of cortical activity to text with an encoder–decoder framework」 脳内で考えていることをテキストなりに具体化する研究についての文書。リカレント・ニューラル・ネットワークの技術を使っているらしい。 → https://t.co/iNEbl5x8Dn
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    August 10, 2020

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    @UCSFPsychiatry (Twitter)

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    July 14, 2020

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    RT @SpringerNature: One of our top rated articles on @altmetric this past week was the Technical Report 'Machine translation of cortical ac…
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    July 5, 2020

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    June 30, 2020

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  • Informatism
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    June 25, 2020

  • The Regeneration Center
    @stemcellscenter (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework using signals from ~250 electrodes distributed over peri-Sylvian cortices of the brain #biotech #science #ML
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    June 23, 2020

  • Brad Neuberg
    @bradneuberg (Twitter)

    “Machine translation of cortical activity to text with an encoder–decoder framework | Nature Neuroscience” They create a neural activity to text brain machine interface by treating it as a sequence 2 sequence problem, neural activity to speech. https://t.co/ndGnBIzf3w
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    June 20, 2020

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  • Tom Lilja
    @tom_lilja (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework https://t.co/fVhNkslep1 Joseph G. Makin , David A. Moses, and Edward F. Chang Chang Lab: https://t.co/K03U2V3SXz Edward F Chang: https://t.co/gUY8iKv1V9
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    May 19, 2020

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    May 16, 2020

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    May 16, 2020

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  • Dr Andree Bates
    @DrAndreeBates (Twitter)

    Cortical activity direct to text! Machine translation of cortical activity to text with an encoder–decoder framework - check out the article in nature. Very nice. #artificialintelligence #ai #brain #neuroscience #brainscience https://t.co/rr0YmdHS0p
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    May 14, 2020

  • fuzzyLife
    @animesh1977 (Twitter)

    Mind Reading, Literally Speaking ;) #Chimpsky Machine translation of cortical activity to text with an encoder–decoder framework https://t.co/zPagT5zYXw
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  • Jake Vikoren
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    This paper: "Machine translation of cortical activity to text with an encoder–decoder framework" scores an impressive 3% word error rate using a relatively network architecture. Wow! note:The model is only drawing from a 250 word vocab https://t.co/dLMvK5QYy8
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  • 机器之心 JIQIZHIXIN
    @jiqizhixin (Twitter)

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    April 27, 2020

    1

  • Cameron
    @ix_cmh (Twitter)

    @paulsoulos @meganakpeters @KordingLab @ylecun @geoffreyhinton @GaryMarcus Potentially relevant new paper from the Chang lab here at UCSF "Machine translation of cortical activity to text with an encoder–decoder framework" https://t.co/OPtnzCpptp
    view full post

    April 27, 2020

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  • Congping Chen
    @CongpingC (Twitter)

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    April 27, 2020

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    @labs_yy (Twitter)

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    @BellSailing (Twitter)

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    @BrianRoemmele (Twitter)

    “Machine translation of cortical activity to text with an encoder–decoder framework” This spectacular paper just published shows a system that reads your inner monologue of Broca and Warnike / phonological loop. Robust evidence we are #VoiceFirst https://t.co/IJN083wDit https://t.co/0y2U7MNrtT
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    April 7, 2020

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  • Dr. Redha Messaoudi
    @DrRedh (Twitter)

    RT @mufeed_AR: تمكن العلماء أخيراً من النجاح في تحويل الذبذبات العصبية للدماغ إلى كلام مفهوم، وذلك عن طريق خوارزميات ترجمة آلية. هل سنرى ف…
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    @ezurysanusi (Twitter)

    RT @zamirmohyedin: Rujukan: Makin, J. G., Moses, D. A., & Chang, E. F. (2020). Machine translation of cortical activity to text with an en…
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    April 7, 2020

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  • مُـ ـفـ ـيـ ـد
    @mufeed_AR (Twitter)

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  • Zamir Mohyedin
    @zamirmohyedin (Twitter)

    Rujukan: Makin, J. G., Moses, D. A., & Chang, E. F. (2020). Machine translation of cortical activity to text with an encoder–decoder framework (pp. 1-8). Nature Publishing Group.
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    April 6, 2020

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  • Targeted Individuals in JAPAN
    @TI_in_JAPAN (Twitter)

    #集団ストーカー　#テクノロジー犯罪 続き #思考盗聴 エンコーダー/デコーダーフレームワークによる皮質活動のテキストへの機械翻訳 Machine translation of cortical activity to text with an encoder–decoder framework | Nature Neuroscience https://t.co/ebsz5mQ9aB
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    April 4, 2020

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  • Sahba Ferdowsi DO (conciergedoc)
    @DrFerdowsi (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework | @Nature // cc ⁦@DeepMind⁩ https://t.co/3Ua4ZRODeH
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    April 3, 2020

  • VictoryHabitats
    @VHabitats (Twitter)

    RT @SpringerNature: One of our top rated articles on @altmetric this past week was the Technical Report 'Machine translation of cortical ac…
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    April 3, 2020

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  • Springer Nature
    @SpringerNature (Twitter)

    One of our top rated articles on @altmetric this past week was the Technical Report 'Machine translation of cortical activity to text with an encoder–decoder framework', published in @NatureNeuro. You can read it here: https://t.co/BevlMC0pY2
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    April 3, 2020

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  • ❁
    @mad_maggs (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework | Nature Neuroscience https://t.co/9v53FCNMNK
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    April 3, 2020

  • Pablo Torres-Carrión
    @pavlvstc (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework https://t.co/mkzu1hi6DO
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    April 2, 2020

  • Prakhar
    @mag_ops (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework | Nature Neuroscience https://t.co/UyeN58nCqi
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    April 2, 2020

  • Marcia... #EmergingTech #URFutureTech
    @MFuturetech (Twitter)

    RT @BioDecoded: Machine translation of cortical activity to text with an encoder–decoder framework | Nature Neuroscience https://t.co/isXpC…
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    April 2, 2020

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  • BioDecoded
    @BioDecoded (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework | Nature Neuroscience https://t.co/isXpC9K8Xp #neuroscience #MachineLearning https://t.co/YIHv7KG2gu
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    April 2, 2020

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  • Ricardo B�nffy -
    @0xDEADBEEFCAFE (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework | Nature Neuroscience https://t.co/5UMVZQLaIu
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    April 2, 2020

  • Self-Regulation Central
    @selfregkids (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework | Nature Neuroscience https://t.co/0y15rRXx9B
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    April 2, 2020

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  • Um Meerh
    @ummeera_ (Twitter)

    RT @belkacem011: Scientists develop AI that can turn brain activity into text.... Read more: Machine translation of cortical activity to te…
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    April 2, 2020

    3

  • Torrez (COVID Hoax Grief Counselor)
    @shartstorm8d8 (Twitter)

    Machine translation of cortical activity to text with an encoder decoder framework - https://t.co/5IHCSMm7Zg
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    April 1, 2020

  • Patrick M. Behuhuma
    @patlovelybhh (Twitter)

    RT @belkacem011: Scientists develop AI that can turn brain activity into text.... Read more: Machine translation of cortical activity to te…
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    April 1, 2020

    3

  • Dr. Belkacem
    @belkacem011 (Twitter)

    Scientists develop AI that can turn brain activity into text.... Read more: Machine translation of cortical activity to text with an encoder–decoder framework, Nature Neuroscience. https://t.co/VFwGBwsFxg #BCI #ECoG #AI https://t.co/7quxbcF0mW
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    April 1, 2020

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  • H+ Bot
    @HplusBot (Twitter)

    Check out "Machine translation of cortical activity to text with an encoder–decoder framework" at: https://t.co/nGo0hpqi0h
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    April 1, 2020

  • Anatoly Zhirkov/Жирков Анатолий
    @AnaZhirkov (Twitter)

    A new step in the development of AI. Machine translation of cortical activity to text with an encoder–decoder framework: https://t.co/cos3DXO0mN
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    April 1, 2020

  • WaterCooler.FM
    @WCNeuro (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework | Nature Neuroscience https://t.co/m1LVgW456F This is very next level neuroscience with machines reading sentences out of our minds from our brain data, however it does have some very funny errors
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    April 1, 2020

  • Ricardo B�nffy -
    @0xDEADBEEFCAFE (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework | Nature Neuroscience https://t.co/5UMVZQLaIu
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    April 1, 2020

  • Paul McCullough
    @ThePostsynaptic (Twitter)

    RT @ThePostsynaptic: Machine translation of cortical activity to text with an encoder–decoder framework https://t.co/Qb7mDufeMS
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    April 1, 2020

    1

  • Keita Mizushima | シェルシステム代表
    @0xb5951 (Twitter)

    @yuji_ikegaya 拙稿ですが、ちょっとまとめてみました。 https://t.co/uVyVvEpuE0
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    April 1, 2020

    1

  • Chen Levkovich
    @chenlev (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework https://t.co/OF1yyYpRdP
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    March 31, 2020

  • NeuroPsych
    @DrEmreMisir (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework https://t.co/1V87PVaN2R
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    March 31, 2020

  • Ricardo B�nffy -
    @0xDEADBEEFCAFE (Twitter)

    Machine translation of cortical activity to text with an encoder–decoder framework | Nature Neuroscience https://t.co/5UMVZR2M74
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    March 31, 2020

  • Pavel Egorkin
    @pavelegorkin (Twitter)

    Пишут, что обучили нейроночку формировать текстом фразы которые думает пациент с трёх процентной ошибкой. Правда пока только с датчиками в мозге и на очень ограниченном словаре. Ждём Нейралинковский вживлятель и здравствуй будущее с телепатией и всяким https://t.co/lmsiiEOb6w
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    March 31, 2020

Abstract Synopsis

  • The study demonstrates a method to accurately translate brain signals from the cortex into spoken English at natural speech speeds using an encoder-decoder neural network model inspired by machine translation techniques.
  • The model trains on electrocorticogram data from participants who repeat a set of sentences, achieving very low error rates (as low as 3%), showing significant progress in decoding brain activity into language.
  • Transfer learning is utilized to enhance decoding performance when data is limited, by training parts of the neural network across multiple participants, improving the model's adaptability and accuracy.]