人工智能的下一個前沿領域是人腦

如果不了解腦機接口，就無法了解人工智能的長期未來。

為什么會這樣呢？因為腦機接口（BCI）將在定義人類智能和人工智能如何在擁有強大人工智能的世界中融合方面發揮核心作用。

對大多數人來說，腦機接口聽起來像是科幻小說。但這項技術正在迅速成為現實。腦機接口在實際功能和應用方面正接近一個轉折點。雖然聽起來有些不可思議，但像心靈感應這樣的能力很快就會成為現實。

腦機接口（BCI）領域可以分為兩大類：侵入式方法和非侵入式方法。侵入式BCI方法需要手術，即將電子設備植入顱骨內，直接植入大腦內部或表面。而非侵入式方法則依賴于位于顱骨外部（例如耳機或帽子上）的傳感器來解讀和調節大腦活動。

在本系列文章的第一部分（10 月份發布）中，我們深入探討了侵入式腦機接口技術和相關初創公司。在本文中，我們將重點關注非侵入式腦機接口。

腦機接口（BCI）和人工智能（AI）的結合，將在未來幾年重塑人類和文明。現在正是我們認真關注這項技術的時候。

傳感器的寶庫

在深入了解當今非侵入式腦機接口 (BCI) 初創企業格局之前，讓我們先花點時間探索一下使非侵入式腦機接口成為可能的核心技術。

無論你用大腦做什么——思考、閱讀、說話、移動手臂——大腦內部都會發生一些可感知的物理事件，并遵循一定的模式。具體來說，信息通過微小的電脈沖在大腦神經元之間流動：這種基本的物理力也驅動著燈泡、廚房電器和iPhone等設備。這些微小的電信號還會觸發大腦中的其他物理活動，包括磁場和血流的變化。

這些物理變化最終代表著信息。它們的模式編碼著思想、概念、語言和行為。而編碼的信息可以被解碼。這正是腦機接口的目標。

為了以不同的方式解讀（“讀取”）和調節（“寫入”）大腦的物理活動，人們開發了多種不同的非侵入式傳感器。每種傳感器都有其優勢和劣勢。為了理解非侵入式腦機接口（BCI）領域，必須了解這些不同類型的傳感器（也稱為“模態”）及其工作原理。

世界上最古老的腦電傳感器是腦電圖（EEG）。腦電圖于1924年在德國發明，如今仍然是世界上應用最廣泛的腦電傳感器。

腦電圖（EEG）通過放置在頭皮上的小型電極直接測量大腦的電活動。（電極是一種可以檢測電活動的簡單裝置。）腦電圖在時間精度方面非常高：它可以以毫秒級的精度測量神經元活動。此外，它還具有價格低廉、便攜、安全且易于使用等優點。

腦電圖最大的缺陷在于其空間定位的不精確性。大腦的電信號在穿過顱骨和頭皮到達腦電圖電極的過程中會發生嚴重的失真，導致難以精確定位其在大腦中的起源位置。這是因為顱骨和大多數骨骼一樣，導電性很差。

此外，腦電圖測量的信噪比很低，因為大腦微弱的電脈沖很容易被附近許多其他電活動源所掩蓋，例如：咬緊牙關、心跳，或者僅僅是環境電磁干擾。僅僅是眨眼就能產生比大腦電信號強10到100倍的電活動。

因此，從腦電圖噪聲數據中提取足夠高保真度的信號，一直是腦電圖應用于腦機接口技術的一大障礙。

另一種非侵入式腦機接口技術在這些方面遠優于腦電圖：腦磁圖（MEG）。

你可能還記得高中物理課上講過，電和磁是同一自然現象——電磁學——的兩個統一表現形式。因此，當神經元放電并產生微弱的電信號時，它同時也會產生微弱的磁場。腦電圖（EEG）測量的是電信號；腦磁圖（MEG）測量的是與之相關的磁場。

與電場相比，磁場最顯著的特點是它幾乎可以完全不受干擾地穿過顱骨和頭皮。因此，MEG的空間分辨率和定位精度遠高于EEG。

有什么貓膩？

如今的MEG系統體積龐大，需要磁屏蔽腔和低溫冷卻，耗資數百萬美元。這使得它們對于日常腦機接口應用而言根本不切實際。

但目前正在進行一些前景可觀的研究，旨在使MEG系統更小更便宜。一種基于光泵磁力計（OPM-MEG）的新型MEG展現出巨大的潛力：它可在室溫下工作，體積小巧，可以佩戴在頭部，而且所需的屏蔽強度也較低。

OPM-MEG技術尚未成熟，但未來幾年它有望成為一種重要的新型腦機接口技術，在避免侵入性手術的同時，提供比腦電圖（EEG）更高保真度的腦部數據。

第三種值得一提的非侵入式腦機接口技術是功能性近紅外光譜技術（fNIRS）。

與腦電圖（EEG）測量電活動或腦磁圖（MEG）測量磁活動不同，功能性近紅外光譜（fNIRS）測量的是腦血流量。神經元放電時，血流量會增加，因為放電的神經元需要更多的營養物質。fNIRS傳感器通過顱骨向大腦發射高波長光束，可以檢測腦血流量的變化，并利用這些變化模式來解碼腦活動。

近紅外光譜成像（fNIRS）如今已成為全球第二大最常用的非侵入式腦機接口（BCI）傳感器，僅次于腦電圖（EEG）。這很大程度上要歸功于布萊恩·約翰遜（Bryan Johnson）創立的初創公司Kernel在過去十年中所做的努力。Kernel的關鍵成就在于實現了fNIRS技術的微型化，首次將其轉化為可穿戴設備，并實現了規模化商業化。與EEG一樣，fNIRS安全、便攜且價格相對低廉。fNIRS在定位方面比EEG更精確，但在時間精度方面則不如EEG；因此，這兩種成像方式互為補充，并經常結合使用。

這就引出了目前最熱門、最有前景的非侵入式腦機接口技術：聚焦超聲。本文將更詳細地探討超聲技術。請繼續閱讀！

要了解非侵入式腦機接口（BCI）領域的最新進展——哪些技術可行，哪些技術不可行，以及未來最大的機遇在哪里——最好的方法是探究當今領先的初創公司正在做的事情。讓我們深入了解一下。

利用腦電圖讀取思想

一群低調的初創公司認為，不起眼的腦電圖 (EEG) 有望從一種為人熟知但功能有限的傳感器轉變為腦機接口 (BCI) 的主流方法。

腦電圖有很多優勢。然而，幾十年來，人們普遍認為腦電圖的信號質量太差，無法支持先進的腦機接口功能。

那么，現代人工智能的一大優勢就是它擁有超乎人類的能力，能夠從嘈雜的數據中提取潛在信號，這真是太方便了。

如果你是一位鐵桿深度學習信徒——一位“苦澀教訓”的極端主義者——那么選擇腦電圖作為你的腦機接口（BCI）模式是有充分理由的。一言以蔽之：規模優勢。

當前人工智能時代的特征在于規模化原則。OpenAI 在 2020 年普及了“規模定律”的概念：人工智能系統會隨著訓練數據、模型規模和計算資源的增加而穩步提升。此后五年間，人工智能的飛速發展主要歸功于規模的全面擴展。大型語言模型之所以如此強大，是因為我們已經掌握了如何利用人類歷史上幾乎所有書面文本來訓練它們的方法。

如果想把在生成式人工智能領域行之有效的策略應用到理解人腦，關鍵在于盡可能多地收集腦訓練數據。而要收集盡可能多的腦訓練數據，最佳傳感器的選擇顯而易見：腦電圖（EEG）。簡而言之，腦電圖比任何其他腦機接口（BCI）模式都更具可擴展性。

如今，全球腦電圖（EEG）系統的數量比其他所有腦機接口（BCI）傳感器加起來還要多幾個數量級。世界上大多數醫院都配備了腦電圖設備；相比之下，全球范圍內功能性近紅外光譜（fNIRS）系統可能只有幾千套，腦磁圖（MEG）系統也只有幾百套。基礎型腦電圖系統的價格不到1000美元。

Conduit是一家年輕的初創公司，它以人工智能為先導、規模化為先導，致力于開發非侵入式腦機接口（BCI）。這家公司由一位牛津大學的年輕研究員和一位劍橋大學的年輕研究員聯合創立，旨在以最快的速度收集盡可能多的數據，以訓練一個大型的大腦基礎模型。該公司表示，到今年年底，他們將收集到來自數千名參與者的超過1萬小時的腦電波記錄。

雖然 Conduit 主要專注于收集腦電圖數據，但它也通過其他非侵入式方式進行補充，因為該公司發現，如果使用來自每個用戶的多種傳感器方式進行訓練，其人工智能的性能會顯著提高，而不是僅僅使用一種傳感器方式。

Conduit 設想其技術有哪些應用場景？

令人驚訝的是，該公司的目標是打造一款腦機接口產品，能夠在用戶將想法轉化為語言之前就解碼他們的想法。換句話說，他們正致力于開發意念轉文本人工智能。

據該公司稱，該系統已經開始運行。Conduit 目前的 AI 模型生成的文本輸出與用戶的想法在語義上匹配度約為 45%，而且無需事先針對任何特定個體進行微調即可實現。

舉幾個具體的例子能讓這一點更具體一些。

例如，當一位參與者想到“房間似乎更冷了”這句話時，人工智能生成了“有微風，甚至一陣輕柔的風”。在另一個例子中，參與者想到“你有沒有最喜歡的應用程序或網站”，人工智能生成了“你有沒有最喜歡的機器人”。

這項技術尚未成熟，無法投入市場。45% 的準確率對于大眾市場產品來說遠遠不夠。而且，目前只有用戶在頭上佩戴一套笨重的傳感器才能達到這樣的準確率。但考慮到這項技術的目標是讀取人心，這樣的準確率仍然令人矚目。而這家公司才剛剛起步。Conduit 公司幾個月前才開始擴大數據收集規模；該公司計劃未來將其訓練數據集擴大幾個數量級。

想象一下，如果僅僅通過思考就能將細微的想法傳達給其他人或計算機，那將會有多么大的可能——社會將會發生怎樣的變化。

Conduit聯合創始人里奧·波普爾表示：“過去十年機器學習領域給我們帶來的最大教訓是規模和數據的重要性。與所有數據集中的個體都必須先接受腦部手術的情況相比，非侵入式方法使我們能夠收集到更大、更多樣化的數據集。”

她的聯合創始人克萊姆·馮·施滕格爾補充道：“我們創立Conduit是因為我們意識到，如果我們都直接用想法而不是語言思考，人們就能更快地完成事情。而且我們也能更深入地了解彼此以及整個世界。”

另一家有趣的年輕創業公司 Alljoined 也在不斷突破腦電圖技術的應用極限。

與 Conduit 類似，Alljoined 也采用了以人工智能為先導的非侵入式腦機接口 (BCI) 技術，并押注腦電圖 (EEG) 是合適的模態，因為它具有可擴展性和易用性。Conduit 的目標是將想法解碼為語言，而 Alljoined 的初期重點是將想法解碼為圖像——也就是說，根據腦電圖讀數忠實地再現用戶“腦海中”的圖像，這項任務被稱為圖像重建。

Alljoined 的首席執行官兼聯合創始人 Jonathan Xu 是開創性論文MindEye2 的合著者之一，該論文表明，基于生成式人工智能的方法僅需少量 fMRI 數據即可實現精確的圖像重建。Alljoined 致力于將這項工作從 fMRI 擴展到 EEG 數據，并且已經取得了成功。

下圖展示了Alljoined人工智能系統利用參與者腦電圖數據重建的部分圖像示例。正如您所見，重建結果并非完全精確，但這些結果代表了目前最先進的性能。而且——正如我們在人工智能的許多其他領域所觀察到的那樣——隨著訓練數據和計算規模的擴大，系統的性能必將持續提升。

上排代表人類參與者觀看的圖像，下排代表 Alljoined 的 AI 系統根據參與者的腦電圖數據重建的圖像。

來源：Alljoined

說到訓練數據，Alljoined 去年開源了首個專門用于腦電圖圖像重建的數據集。該數據集包含 8 位參與者的腦電圖數據，每位參與者觀看 10,000 張圖像。免費提供這些數據應該會極大地推動整個領域的發展。

Alljoined 最初專注于圖像重建，但該公司也在探索其他應用領域。其中一個極具前景的領域是情感分析——即實時、精準地識別用戶正在經歷的情緒。直接從腦電數據中解碼情感具有重要的商業價值，例如在市場營銷和消費者行為研究領域，而且比目前讓人們自我報告情緒的方式更加準確可靠。

最后值得一提的還有一家總部位于以色列的腦電圖初創公司 Hemispheric。

Hemispheric公司由蘋果Face ID技術的聯合創始人之一創立，正全力探索腦電圖（EEG）的可擴展性規律。該公司正在世界各地建立腦電圖數據采集設施，并對這些設施的搭建方式進行系統化和模塊化設計，以期盡快實現規模化。

這家公司計劃在未來幾個月內結束隱秘運營，多年來一直致力于開發一種新型模型架構，用于訓練最先進的基礎腦電圖模型。該公司最近成功擴展并訓練了其首個數十億參數模型。

“一些公司專注于開發改進型非侵入式傳感器，押注更先進的硬件將解鎖高精度非侵入式腦機接口（BCI）產品，”Hemispheric 首席執行官兼聯合創始人 Hagai Lalazar 表示。“我們則持相反觀點：我們認為現有的非侵入式傳感方式（腦電圖、腦磁圖、功能性近紅外光譜）已經足夠，突破將并非來自更先進的傳感技術，而是來自對現有信號的更精準解碼。人工智能是算法史上最偉大的革命，但迄今為止，還沒有人能夠大規模地收集腦活動數據并訓練模型來解碼神經數據。我們相信，在開發用于解碼大腦電活動‘語言’的人工智能方面取得突破，是實現非侵入式腦機接口普及的關鍵所在。”

從更宏觀的角度來看，值得注意的是，對于腦電圖（EEG）與尖端人工智能相結合能否實現本文所述的宏偉愿景，仍然存在諸多不確定性和質疑。許多觀察人士對能否從腦電圖讀數中提取足夠高的信號數據以支持高級腦機接口（BCI）應用持懷疑態度，甚至完全否定這種觀點。這種質疑主要來自那些專注于侵入式腦機接口方法的人、那些幾十年來親身經歷并運用腦電圖局限性的人，以及那些并非來自深度學習領域的人士。此外，一些近期研究也對利用腦電圖進行語言解碼的進展提出了質疑。

懷疑論者或許是對的。

然而，現實情況是，無論是懷疑論者、這些以人工智能為先導的腦電圖初創公司，還是世界上任何一位腦機接口或人工智能專家，都無法確定答案。目前世界上還沒有人大規模收集腦電圖訓練數據，并用這些數據訓練大型神經網絡，評估其性能。也沒有人能夠最終驗證或證偽腦電圖基礎模型是否存在像大型語言模型那樣的擴展規律這一假設。

2018 年OpenAI發布第一個 GPT 模型時，沒有人能夠想象，也沒有人會相信，在接下來的幾年里，僅僅通過規模化就能帶來如此驚人的性能提升。

只有時間才能證明，在腦機接口（BCI）領域，規模化能否像在機器學習（LLM）領域那樣卓有成效。如果確實如此，那就不要忽視腦電圖（EEG）技術。

用于神經調控的消費級可穿戴設備

從 Fitbit（被 Google 以 21 億美元收購）到 ōura（最近估值 110 億美元）再到 Apple Watch（年收入超過 100 億美元），近年來許多消費可穿戴產品都取得了突破性的成功。

所有這些消費級可穿戴產品有什么共同點？它們都能測量你的個人健康指標，但無法改變這些指標。它們只能“讀取”數據，而不能“寫入”數據。（上文討論的腦電圖應用案例也同樣只涉及讀取，而不能寫入。）

新一代消費級可穿戴設備公司正在打造以大腦為中心的產品，這些產品不僅能監測大腦狀態，還能主動調節大腦活動。如果這些產品真能如預期般發揮作用，不難想象，其中一款產品可能會成為下一個ōura。

一個有趣的例子是 Somnee Sleep，這是一家初創公司，它制造了一種頭帶，旨在改善用戶的睡眠質量。

Somnee 由四位世界頂尖的睡眠科學家共同創立，其中包括加州大學伯克利分校教授馬修·沃克博士，他是頗具影響力的著作《我們為什么要睡覺》的作者。

睡眠是人類最普遍、最重要的精神活動。一款能夠顯著改善用戶睡眠的消費產品，將蘊藏著巨大的市場機遇：據統計，每年用于安眠藥的支出高達800億美元。

Somnee是如何運作的？

Somnee的頭帶利用腦電圖（EEG）和其他傳感器追蹤睡眠期間的大腦活動，并通過人工智能學習您特定的睡眠模式和信號。然后，它會發出個性化的電脈沖，引導您的腦電波進入最佳節律，從而獲得更深層、更高效的睡眠。這種神經調節技術被稱為經顱電刺激（tES）。

研究表明，Somnee 的消費者頭帶在改善睡眠方面比褪黑素有效四倍，比安眠藥（如安必恩）有效 1.5 倍。

來源：Somnee Sleep

它真的有效嗎？

同行評審的研究表明確實如此。

最近一項臨床研究表明，Somnee 的產品在提高睡眠效率方面比褪黑素有效四倍，比安眠藥（如安必恩）有效 50%。

在該公司最近完成的另一項研究中，Somnee 的頭帶幫助用戶入睡速度提高了一倍，睡眠時間延長了 30 多分鐘，翻身次數減少了三分之一。

美國國家籃球協會（NBA）近日宣布與Somnee公司合作，將該公司的產品提供給NBA球員。Equinox健身中心和酒店也將很快提供Somnee的頭帶。

該領域另一家值得關注的初創公司是總部位于英國的Flow Neuroscience。與Somnee類似，Flow的產品也是一款可穿戴頭帶，它利用經顱電刺激技術產生輕柔的個性化電脈沖，從而調節用戶的大腦活動。但Somnee專注于改善睡眠，而Flow的產品則旨在對抗抑郁癥。

抑郁癥會影響大腦中一個關鍵區域，即背外側前額葉皮層。抑郁癥患者的該區域腦細胞活動減弱。Flow 的頭帶可將精確校準的電刺激直接輸送到背外側前額葉皮層，從而刺激該區域并恢復健康的腦細胞活動模式。

Somnee 和 Flow 都依賴于經顱電刺激 (tES)。但 Somnee 使用的是經顱交流電刺激 (tACS)，而 Flow 使用的是經顱直流電刺激 (tDCS)。它們之間有什么區別呢？簡而言之，像 Flow 這樣的直流電產品會向大腦提供恒定電流，使神經元更容易放電；而像 Somnee 這樣的交流電產品則會引入振蕩脈沖，從而影響神經元放電的節律和頻率。

與Somnee一樣，Flow產品的療效也已在同行評審的研究中得到驗證。去年發表在《自然醫學》雜志上的一項大型臨床試驗發現，Flow產品在治療抑郁癥方面的療效是抗抑郁藥物的兩倍。該研究顯示，57%使用Flow產品的臨床抑郁癥患者在10周后表示抑郁癥狀已消失。該公司報告稱，在其數萬名用戶中，超過75%的用戶在三周內就感受到了臨床癥狀的改善。

Flow公司將其產品描述為“以電療為藥”，這個說法非常貼切。

Somnee 和 Flow 的頭帶均可在網上向公眾購買。

最后值得一提的初創公司是Neurode。Neurode的頭帶利用電刺激來提高用戶的專注力和注意力。該產品既適用于患有注意力缺陷多動障礙（ADHD）的人群，也適用于希望提升整體認知功能的普通人群。

Flow采用經顱直流電刺激（tDCS，一種恒流刺激），Somnee采用經顱交流電刺激（tACS，一種節律性振蕩電流），而Neurode則采用經顱隨機噪聲刺激（tRNS），它提供的電流頻率和振幅均隨機波動。新興研究表明，引入這種隨機噪聲可以增強神經回路中的信號檢測能力，從而改善學習和注意力。

據該公司稱，45% 的用戶在使用該產品的第一周內就感受到注意力有所提高。

新興的臨床研究表明，像這些公司正在研究的那種使用消費級硬件對大腦進行電刺激，確實可以對大腦行為和個人體驗產生顯著影響，影響領域涵蓋睡眠、抑郁和注意力等諸多方面。

“這些初創公司正值良機，”美國食品藥品監督管理局（FDA）數字健康部門前駐場企業家安德里亞·科拉沃斯補充道，“監管體系尚未跟上步伐。FDA的首個人工智能/機器學習框架于2019年發布，此后已有近1000種人工智能設備獲得批準。正是這一監管基礎，使得企業能夠更快地將研究成果應用于實際人體。”

但這些產品目前都尚未獲得主流市場的認可。這些公司能否打造出足夠令人愉悅的產品體驗和足夠有效的市場推廣策略，從而將這些設備推向大眾市場并獲得成功，時間會給出答案。

聚焦超聲：下一個偉大的腦機接口范式？

如果說有一種腦機接口技術最具發展潛力——一種能夠超越現有解決方案（包括本文討論的方案）并引領神經技術新范式的方案——那就是聚焦超聲。在當今腦機接口領域，沒有哪個方向比它更能引起人們的關注和興奮。

聚焦超聲究竟是什么？它為何如此具有發展前景？

從根本上講，超聲波只是聲音的一個子類——也就是說，它是能在空氣和其他物質的粒子中傳播的波。人類可以聽到特定頻率范圍內的聲波。超聲波就是頻率高于人類耳朵可聽范圍（>20千赫茲）的聲波，但其傳播特性與可聽聲波類似。

超聲波技術已用于醫學成像超過 75 年（任何懷孕過或有親人懷孕的人都會記得這一點）。

腦部聚焦超聲是一項較新的創新技術——直到 2010 年代才開始逐漸成形。

聚焦超聲的基本原理是精確地發射多束超聲波，使它們匯聚于大腦中的某一特定點。所有超聲波在該焦點處疊加，產生足夠的能量密度和機械壓力，從而以特定的方式調節該點的神經元活動，同時不影響超聲波穿過的其他腦組織。（本頁上的兩個簡單動畫圖很好地展示了這一現象，使其易于理解。）

聚焦超聲作為一種腦機接口技術，具有幾個獨特且引人注目的優勢。

首先，它的精度比任何其他非侵入式腦機接口技術都要高出幾個數量級。腦電圖（EEG）、功能性近紅外光譜（fNIRS）和經顱電刺激（tES）的空間分辨率都只有幾厘米。相比之下，聚焦超聲可以以亞毫米級的精度靶向大腦中的特定區域。它可以被視為一束高精度光束，可以精確地瞄準大腦中想要定位的位置。

其次，聚焦超聲比任何其他非侵入性技術都能更深入地進入大腦。

由于非侵入式傳感器位于顱骨外，它們通常只能探測并與大腦最外層（即新皮層）進行交互。新皮層是高級認知和語言功能的中心，因此，能夠探測到新皮層的傳感器可以實現許多有用的應用。但是，許多重要的腦區和功能位于大腦更深層，因此腦電圖（EEG）、功能性近紅外光譜（fNIRS）、經顱電刺激（tES）和其他非侵入式傳感器無法觸及。

丘腦、下丘腦、海馬體、基底神經節和杏仁核等深層腦結構調節著我們許多基本驅動力和功能：情緒、記憶、注意力、食欲、情緒、運動、動機和渴望。精準調控這些深層腦區的能力，有望為帕金森病、強迫癥、抑郁癥、阿爾茨海默病、癲癇、焦慮癥、慢性疼痛和創傷后應激障礙等多種神經精神疾病帶來強有力的新療法——更不用說還能為普通人群帶來認知增強。

此前，只有通過手術等侵入性方法，例如腦深部刺激（DBS），才能到達這些更深層的腦區。除超聲波之外的所有非侵入性療法——無論是電波、磁波、光波還是紅外波——都會被人體組織衰減，這意味著它們只能傳播有限的距離就會消散。相比之下，聚焦超聲波是一種機械波，因此可以幾乎不受組織衰減的影響地穿過人體組織。這使得它能夠在保持高度聚焦的同時，深入大腦內部。

這些可能性并非僅僅停留在理論層面。近期研究表明，聚焦超聲可以顯著減輕患者的慢性疼痛；降低嚴重成癮者的阿片類藥物渴求；并最大限度地減少特發性震顫患者的震顫癥狀——所有這些都涉及對大腦深部結構的激活。

超聲波的最后一個優勢使其區別于其他所有非侵入性檢查方式：它既能讀取也能寫入，而且都能以高分辨率完成這兩項操作。沒有任何其他單一的非侵入性檢查方式能夠同時實現這兩項功能。腦電圖（EEG）、功能性近紅外光譜（fNIRS）和腦磁圖（MEG）可以讀取數據，但不能寫入數據。經顱電刺激可以寫入數據（盡管分辨率和深度均低于聚焦超聲），但不能讀取數據。

讀寫能力解鎖了腦機接口的圣杯：閉環功能，即一個統一的系統可以讀取和解碼正在進行的神經活動，然后根據讀取的內容以選擇性和個性化的方式刺激大腦，然后觀察大腦如何實時響應和適應，等等。

與使用一個設備進行傳感，另一個設備進行調制相比，一個既能讀取又能寫入的設備可以實現傳感和刺激之間的完美對齊、低延遲、簡單的校準、更少的硬件復雜性、更高的空間效率、更低的成本，并最終實現更具可擴展性的產品。

超聲波腦機接口領域的創業環境尚處于起步階段，但發展速度驚人。

目前最受矚目的聚焦超聲初創公司是 Nudge，該公司最近宣布完成由 Thrive 和 Greenoaks 領投的 1 億美元融資。

Nudge 的首席執行官兼聯合創始人 Fred Ehrsam 此前曾聯合創立 Coinbase 和 Paradigm，這兩家公司都是加密貨幣領域最成功的企業之一。Nudge 由此延續了億萬富翁創辦腦機接口 (BCI) 初創公司的傳統，此前 Elon Musk 創立了 Neuralink，Bryan Johnson 創立了 Kernel，Sam Altman 創立了 Merge Labs（下文將詳細介紹 Merge）。Nudge 的另一位聯合創始人 Jeremy Barenholtz 此前曾領導 Neuralink 的產品和技術工作。

Nudge 的使命是推進聚焦超聲技術在硬件、人工智能和神經科學領域的全面發展，從而實現精準、強大的非侵入式神經調控。公司初期專注于治療成癮、慢性疼痛和焦慮等神經精神疾病，但其最終目標是讓大眾都能增強認知能力。Nudge 致力于讓每個人都能精準、便捷地調節自身在學習、記憶和睡眠等領域的心理行為。

Nudge 的初始形態是一個嵌入核磁共振成像儀中的超聲頭盔。（核磁共振成像儀用于“讀取”圖像。雖然超聲本身也可以用于高分辨率讀取，但 Nudge 最初的核心重點是推進聚焦超聲“寫入”技術的最新發展。）

該公司的產品功能齊全，幾乎每天都被用于人體研究。這款初始產品不便攜帶，不適合消費者使用，但Nudge公司已經在研發一款更小巧的架構，旨在方便在家中和日常生活中使用。

Nudge 的首款聚焦超聲設備是 Nudge Zero。

來源：Nudge

正如該公司所說：“想象一下，未來無需阿片類藥物即可緩解慢性疼痛，創傷后應激障礙患者可以實時調節創傷記憶，臨床醫生可以像檢查患者心率一樣輕松地對大腦回路進行成像和調節。想象一下，未來無需咖啡因即可提高注意力，學習一門新語言或一項新技能只需幾天或幾周，而不是幾個月或幾年。這并非科幻小說，而是一份工程路線圖。而我們正在著手實現它。”

該領域另一家頗具潛力的初創公司是Sanmai，由亞利桑那大學教授、聚焦超聲技術早期先驅Jay Sanguinetti領導。Sanmai的主要投資人是Reid Hoffman，他主導了該公司近期1200萬美元的融資。

與Nudge類似，Sanmai也專注于超聲波的神經調控能力（即其“寫入”大腦的能力），而非其傳感能力（即其“讀取”大腦的能力）。與Nudge相比，Sanmai更注重嚴謹的臨床應用，較少面向消費者。

三麥的經顱聚焦超聲設備目前正在進行臨床研究，有望成為世界上首個獲得FDA批準的經顱聚焦超聲設備。

Sanmai 的聚焦超聲設備是可穿戴的，最初專注于治療帕金森病。

來源：三枚

三麥制藥的首要治療目標是帕金森病。全球約有1000萬人患有帕金森病，僅美國每年就新增9萬例，這使其成為一個重要的市場機遇。三麥制藥的聯合創始人之一泰勒·庫恩（Taylor Kuhn）發表了最早一批研究成果，證明了聚焦超聲在治療帕金森病方面的療效。

我們尚未探討的一個問題是，聚焦超聲究竟是如何治療帕金森病等腦部疾病的——也就是說，這項技術的作用機制是什么。簡而言之，就像大多數與大腦相關的問題一樣，我們尚未完全了解其中的細節。但帕金森病的案例引人入勝，值得我們深入研究。

帕金森病的主要誘因被認為是大腦深部多個區域神經元內α-突觸核蛋白的錯誤折疊蛋白的積累。研究表明，利用聚焦超聲的集中機械能靶向這些深部腦區，可以減少α-突觸核蛋白的毒性積累，從而可能有助于緩解帕金森病的癥狀。

Sanmai計劃在近期內利用聚焦超聲治療的其他疾病包括臨床焦慮癥，這將涉及針對患者的杏仁核進行治療。

“我大約在15年前就開始研究超聲神經調控，”Sanmai首席執行官兼聯合創始人Jay Sanguinetti說道。“當時，大多數人都懷疑低強度超聲的微弱機械能是否真的能影響大腦活動。作為一名研究生，我閱讀了一些早期的論文——其中一些甚至有近百年的歷史——并感覺其中蘊含著一些真實的東西。早期，我不得不努力爭取讓人們關注這些數據。如今，這個領域已經取得了巨大的進步。我們創立Sanmai的初衷是打造首款專為臨床應用而設計的超聲神經調控設備，它將嚴格的安全標準、人工智能輔助的個性化靶向定位以及實際的臨床應用相結合，旨在讓臨床醫生在診療過程中充滿信心。”

該領域另一家前沿創業公司是 Forest Neurotech。

Forest Neurotech是一家非營利機構——更確切地說，它是一種新型的非營利創業公司，稱為聚焦研究組織（FRO）。FRO是一種創新的新型融資結構，旨在支持那些規模龐大或成本高昂，對傳統學術實驗室而言過于龐大或昂貴，但商業化程度又不足以進入產業界的特定且雄心勃勃的科學里程碑的實現。FRO通常擁有類似創業公司的團隊和文化，但其資金來源是慈善捐贈，而非風險投資。因此，Forest不追求商業化，而是專注于推進基礎超聲技術的尖端發展。

具體來說，福里斯特專注于將超聲波硬件小型化，這是使這項技術得到廣泛應用的關鍵一步。

而且，該公司在這方面取得了令人矚目的成功。Forest公司最近發布了其首款設備——Forest 1腦機接口，該設備比傳統的超聲波掃描儀小1000倍，比標準鑰匙扣還要小。

Forest Neurotech 公司的 Forest 1 設備比標準鑰匙扣還要小，可以使用超聲波進行讀寫，設計用于植入患者的顱骨內。

來源：Forest Neurotech

值得注意的是，Forest 1 設備能夠利用超聲波進行讀寫操作，這使其區別于 Nudge 和 Sanmai 設備。它能夠基于血流動力學生成整個大腦（深度達 20 厘米）的高分辨率三維圖像，并且還可以進行精確的神經調控。

Forest 1 設備凸顯了超聲波技術的一個重要特性。此前，我們一直將超聲波視為一種無需手術的非侵入式腦機接口 (BCI) 技術。事實上，超聲波可以而且經常以非侵入式的方式應用：Nudge 和 Sanmai 都采用了非侵入式的超聲波技術。

但福雷斯特的裝置屬于侵入性操作：需要通過手術切開患者的頭骨，并將裝置植入其中。

這是為什么呢？

顱骨對于超聲波來說是一個很大的挑戰，因此將超聲波設備放入顱骨內有很大的優勢。

超聲波穿過大腦等軟組織時衰減很小，但顱骨則不然。顱骨由骨骼構成，對超聲波的傳播效果很差。顱骨會反射部分超聲波，吸收部分超聲波，還會散射和扭曲剩余的超聲波。

如何解釋超聲波與顱骨相互作用以及受顱骨影響的不可預測性，是聚焦超聲領域面臨的最大未解工程難題之一。像Nudge和Sanmai這樣的初創公司正在投入大量資源來解決這一難題。

Forest公司針對這個問題提出的解決方案是，直接將設備植入用戶的顱骨內。這種方法的優點在于完全避免了超聲波穿過顱骨這一棘手問題。缺點是，任何想要使用Forest設備的患者都必須先接受腦部手術。天下沒有免費的午餐。

Forest公司稱其植入手術為“微創手術”，因為雖然植入設備需要打開患者的顱骨，但該設備不會穿透患者的腦組織；相反，它位于大腦的保護性硬腦膜層之上。這使其與Neuralink和猶他陣列等完全侵入式腦機接口技術截然不同，后者會穿透大腦組織。

FRO（前沿研究組織）通常設定了時間限制，其理念是，如果團隊實現了特定的科研目標，就可以孵化出一家傳統的營利性初創公司，實現商業化。因此，不久之后，如果看到一家或多家營利性初創公司從Forest Neurotech組織中涌現出來，也不要感到驚訝。

Forest聯合創始人威爾·比德曼表示：“多年來，設備小型化和計算能力的提升為我們在醫療保健領域帶來了更強大的技術。現在，借助超聲波技術，我們擁有了實現無創腦機接口夢想所需的保真度、精確度和理解力。”

我們將要討論的最后一家超聲波腦機接口初創公司是所有初創公司中最具雄心和前沿性的：Sam Altman 的 Merge Labs。

Merge公司尚未正式上線，因此目前公開的信息很少。（不過，未來幾天內情況可能會有所改變，請不要感到驚訝！）

據報道，Sam Altman 將擔任該公司聯合創始人之一，OpenAI 已向該公司投入巨資，估值達 8.5 億美元。

Merge公司將以近期超聲波技術的突破為基礎，對人腦進行讀寫操作。但它的目標是進一步拓展這項技術的邊界：公司的愿景是將聚焦超聲波與基因編輯相結合，從而實現更強大的腦機接口（BCI）功能。沒錯，你沒看錯：超聲波加基因編輯！

這是怎么回事？

簡而言之，基因編輯可以使大腦中特定的神經元群以特定方式對聚焦超聲波產生反應。這一新興科學領域被稱為聲遺傳學。

首先，可以通過基因工程將一個特殊基因插入大腦中特定神經元亞群的DNA中。該特殊基因可以編碼一種對機械力敏感的特定蛋白質。由于聚焦超聲會產生微小的機械擾動，因此這些特定神經元中的這種特定蛋白質會對聚焦超聲的作用產生反應。具體來說，這種蛋白質通常是一種離子通道，當受到聚焦超聲作用時，它會按需打開或關閉。

與不涉及基因編輯的聚焦超聲相比，聲致遺傳學方法能夠對大腦活動進行更加精準和個性化的控制。它能夠靶向大腦中的特定神經元和神經元類型，同時不影響其他神經元：例如，僅影響興奮性神經元而不影響抑制性神經元，或者僅影響表達特定受體的神經元，或者僅影響特定的腦回路（例如，與某些成癮行為相關的特定投射通路）。

聲波成像方法還可以更直接地定義和控制聚焦超聲作用于大腦神經元的機制，從而確定其作用效果，并據此將新的基因和蛋白質引入神經元。

加州理工學院著名教授米哈伊爾·夏皮羅是這一新興研究領域的先驅之一。據報道，夏皮羅已加入 Merge Labs，這對該公司來說無疑是一項重大勝利。

即使在腦機接口這一前沿領域，Merge Labs 正在探索的方法也堪稱最具前沿性和“科幻色彩”。一些基本的科學問題仍有待解決。即便最終能夠成功，這一愿景的實現也至少需要十年或更長時間。

無聲的語言

最后值得討論的一種非侵入式創業類別是無聲演講。

無聲語言技術能夠感知并解碼某人試圖說或想象要說的話，即使他們沒有大聲說出這些話。（因此，它也被稱為默讀。）

無聲語言技術與本文討論的其他技術和初創公司有一個關鍵區別：它不涉及直接解碼大腦信號。相反，它關注的是大腦下游的物理信號——特別是與說話意圖相關的面部和嘴部信號。

無聲語言是如何運作的？其基本原理是，當一個人試圖說話時，即使沒有發出任何聲音，其言語系統中的各種電生理和肌肉機制（例如舌頭、嘴唇、下頜）也會啟動。這些生理機制是可以被檢測和解碼的。

目前對于實現無聲說話的最佳技術方案尚未達成共識。不同的公司正在探索不同的方法，而且一般來說，無聲說話初創公司對其技術細節高度保密。我們可以確定的是：從人臉解碼試圖說話或想象說話的物理特征的可行方法包括基于生物磁、光學和射頻數據的技術。

在思考無聲言語時，設想一系列可能性會很有幫助：從（1）完全正常的言語，到（2）耳語但仍然可以聽到的言語，到（3）聽不到但嘴巴完全張開的言語，到（4）部分張開的言語（例如，人的嘴巴保持閉合，但舌頭在嘴里移動），到（5）幾乎不涉及任何身體動作的“言語”，只是在腦海中構思和發出詞語。

所有無聲語音識別公司都在致力于開發能夠解碼低聲語音的技術，即上文第 (3) 類和第 (4) 類語音。無聲語音識別技術能否可靠地破解第 (5) 類語音——通常被稱為“想象語音”——還有待觀察。

近年來，語音作為一種高效、便捷且直觀的交互方式，在人工智能時代迅速普及。無聲語音的優勢在于，它能讓人們以語音作為交互界面——與他人交流、搜索互聯網、記筆記、回復電子郵件等等——而且無論身處何地，無論是在辦公室、擁擠的咖啡館、地鐵還是街頭，都能私密且隱蔽地進行這些操作。

大多數致力于研發無聲語音技術的公司都設想將這項技術嵌入到耳機或藍牙耳機等消費產品中。在產品外形中加入某種耳塞至關重要，因為它能實現私密的低聲輸入與私密音頻輸出的閉環連接——例如，用戶不僅可以隱蔽地查詢人工智能模型，還可以隱蔽地接收回復。

雖然目前有不少前景看好的初創公司正在研發無聲語音技術，但迄今為止只有一家公司公開亮相：那就是麻省理工學院的衍生公司 AlterEgo。AlterEgo 兩個月前發布了一段 3 分鐘的宣傳視頻，值得一看，可以幫助你更直觀地了解無聲語音的概念。

AlterEgo 首席執行官兼聯合創始人 Arnav Kapur 表示：“目前與計算機和人工智能交互的方式受限于你在屏幕和鍵盤上點擊和打字的速度。在智能時代，我們需要一個從零開始構建的全新界面——一個感覺像是人類思維自然延伸的界面。為了實現這一點，我們必須發明一些全新的東西。”

預計到 2026 年，會有更多資金雄厚、實力雄厚的無聲演講競爭者從幕后走向臺前。

坊間盛傳，蘋果和谷歌等科技巨頭正在認真探索將無聲語音功能作為未來消費硬件產品的核心技術。同樣，也有傳言稱，由蘋果前傳奇設計師喬納森·艾維領銜設計的OpenAI即將推出的原生AI消費設備也將具備無聲語音功能。

因此，我們預計在中短期內，該初創企業領域將會出現一些備受矚目的并購交易。

但無聲語言要成為人機交互領域的一個重要新范式，還需要克服一些障礙。

首先，無聲語音產品的廣泛應用將需要消費者行為和社會規范發生重大改變。如今，有多少人會樂意使用一種需要在辦公室或咖啡館里默默地用嘴型說話的產品呢？

無聲言語面臨的更根本風險在于，其他能更直接地與大腦交互的腦機接口技術可能會超越它，并使其功能黯然失色。如果能夠直接從大腦提取高保真度的語言信號——例如，如果人工智能驅動的腦電圖或下一代超聲成像技術能夠充分發揮其潛力（如上所述）——那么我們為什么還要費心研究默念呢？無聲言語或許比可聽見的言語更私密，延遲也比打字更低，但思想的私密性和延遲都遠勝于上述所有方式。

事實上，這些技術都尚未成熟，無法真正投入市場。它們各自發展迅速，潛力巨大，但都可能面臨性能瓶頸或難以實現產品化。這些技術究竟能以多快的速度發展，最終融入人們使用和喜愛的產品中，時間會給出答案。

結論

縱觀人類文明，技術進步的一個顯著特征就是通信和信息傳輸的速度、帶寬和準確性的提升。文字的發明、古騰堡的印刷機、電報、無線電、電話、互聯網——所有這些技術飛躍的本質都是為了增強人類共享信息的能力。

總的來說，當更多的人能夠更有效地相互交流更多信息時，就會帶來各種各樣事先無法預測的積極影響：科學進步、健康進步、生產力提高、教育進步、以及我們對彼此和宇宙的理解加深。

腦機接口代表了數千年來技術進步的必然下一步。

信息在人與機器之間直接往返于大腦是最有效的傳遞方式。它消除了對有損信息的中間環節的需求，包括語言本身。畢竟，語言本身就是一種高度有損的壓縮：想想看，你內心深處的心理體驗，其所有細節，與你能用語言表達的程度之間，存在著多么巨大的差異。

高性能腦機接口（BCI）將開啟各種奇妙而寶貴的可能性。近期影響將體現在醫療領域，這將為全球數百萬患有各種神經精神疾病或心理健康問題的患者帶來深遠的益處。但這僅僅是個開始。試想一下，只需將新技能——比如空手道、潛水或高爾夫——“上傳”到大腦，直接強化相應的神經通路，就能瞬間掌握這些技能。試想一下，能夠以完美的“感官保真度”回憶和重溫任何記憶。試想一下，能夠重新編程大腦，使其看到或感受到如今人類大腦無法直接感知的事物：Wi-Fi信號、無線電波，甚至是“正北”方向。

更重要的是，我們甚至還無法想象腦機接口將帶來的最深刻的變革和機遇——就像十四世紀的人們無法想象印刷書籍將以各種方式改變社會（民主、科學方法、啟蒙運動）；或者 20 世紀 80 年代的人們無法想象互聯網將以各種方式改變社會（比特幣、云計算、優步）一樣。

從長遠來看，腦機接口技術在社會上的普及是不可避免的。然而，目前遠未確定的是，腦機接口技術的主流方法究竟是無創的、有創的，還是兩者兼而有之。

如今，鮮有其他技術領域像腦機接口（BCI）這樣，讓眾多見多識廣的觀察者對該領域的未來發展方向持有如此截然相反的觀點。一些專家基于簡單的物理定律，提出了令人信服的論證，認為最先進的腦機接口技術始終需要與大腦進行直接的物理連接，因此必然需要手術。另一些專家則同樣令人信服地指出，鑒于非侵入式技術在可擴展性、安全性和易用性方面的巨大優勢，它們才是該領域發展的必然趨勢；而且，傳感、解碼和調制技術的進步只是時間問題，最終即使是最先進的腦機接口應用也能以非侵入式的方式實現。還有一些專家則認為，直接作用于大腦本身并非必要，像無聲語音這樣的具有劃時代價值的產品將基于大腦下游的信號構建。

未來幾年，這些技術將從實驗室走向我們生活的方方面面。做好準備吧。

The Next Frontier For AI Is The Human Brain

ByRob Toews,Contributor. I write about the big picture of artificial intelligence.

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Dec 07, 2025, 05:45pm ESTDec 07, 2025, 11:45pm EST

Sam Altman and Elon Musk's rivalry has grown beyond AI to brain-computer interfaces.

SOURCE: GETTY

It is not possible to understand the long-term future of artificial intelligence without understanding brain-computer interfaces.

Why is that? Because brain-computer interfaces (BCI) will play a central role in defining how human intelligence and artificial intelligence fit together in a world with powerful AI.

To most people, brain-computer interfaces sounds like science fiction. But this technology is getting real, quickly. BCI is nearing an inflection point in terms of real-world functionality and adoption. Far-fetched though it may sound, capabilities like telepathy will soon be possible.

The world of BCI can be divided into two main categories: invasive approaches and non-invasive approaches. Invasive approaches to BCI require surgery. They entail putting electronics inside the skull, directly in or on the brain. Non-invasive approaches, on the other hand, rely on sensors that sit outside the skull (say, on headphones or a hat) to interpret and modulate brain activity.

In the first part of this article series, published in October, we dove deep into invasive BCI technologies and startups. In this article, we turn our attention to non-invasive BCI.

Together, BCI and AI will reshape humanity and civilization in the years ahead. Now is the time to start paying serious attention to this technology.

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A Cornucopia of Sensors

Before we walk through today’s non-invasive BCI startup landscape, let’s spend a moment exploring the core technologies that make non-invasive BCI possible.

Whenever you use your brain to do anything—think a thought, read a book, speak a sentence, move your arm—detectable physical events take place inside your brain in certain patterns. Specifically, information flows through your brain’s neurons via tiny pulses of electricity: the same basic physical force that powers lightbulbs and kitchen appliances and iPhones. These tiny electrical signals trigger other physical activities in your brain as well, including changes in magnetic fields and blood flow.

These physical changes ultimately represent information. Their patterns encode thoughts, concepts, words, actions. And information that is encoded can be decoded. That is what brain-computer interfaces seek to do.

A number of different non-invasive sensors have been developed in order to both interpret (“read”) and modulate (“write”) the brain’s physical activities in different ways. Each has strengths and weaknesses. In order to understand the field of non-invasive BCI, it is essential to understand these different sensor types (also referred to as “modalities”) and the mechanisms by which they operate.

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The world’s oldest brain sensor is the electroencephalogram, or EEG. Invented in 1924 in Germany, EEG today remains the most widely used brain sensor in the world.

EEG directly measures electrical activity from the brain using small electrodes placed on the scalp. (Electrodes are simple devices that can detect electrical activity.) EEG is highly precise from a timing perspective: it can measure neuronal activity with millisecond-level accuracy. It is also inexpensive, portable, safe and easy to use.

EEG’s great weakness is how imprecise it is from a spatial perspective. The brain’s electrical signals get heavily distorted as they pass through the skull and scalp on the way to the EEG’s electrodes, making it difficult to pinpoint exactly where in the brain they originated. This is because the skull, like most bone, is a terrible conductor of electricity.

Relatedly, EEG measurements have poor signal-to-noise ratio because the brain’s tiny electrical pulses can easily be drowned out by many other nearby sources of electrical activity: a jaw clenching, a heart beating, or just ambient electromagnetic interference. Simply blinking your eyes can generate electrical activity that is 10 to 100 times stronger than the electrical signals from your brain.

Extracting sufficiently high-fidelity signal from EEG’s noisy data thus represents a long-standing obstacle to using EEG for BCI technology.

Another non-invasive BCI modality is vastly superior to EEG on these dimensions: magnetoencephalography (MEG).

As you may remember from high school physics, electricity and magnetism are two unified aspects of the same underlying natural phenomenon: electromagnetism. So when a neuron fires and generates a tiny electrical signal, it generates a tiny magnetic field at the same time. EEG measures the electrical signal; MEG measures the associated magnetic field.

Compared to electrical fields, the remarkable thing about magnetic fields is that they pass through the skull and scalp almost completely undistorted. As a result, MEG has far greater spatial resolution and localization accuracy than EEG.

What’s the catch?

Today’s MEG systems are room-sized, requiring a magnetically shielded chamber and cryogenic cooling. They cost millions of dollars. This makes them hopelessly impractical for everyday BCI applications.

But promising research is underway to make MEG systems smaller and cheaper. A newer type of MEG based on optically pumped magnetometers (OPM-MEG) shows great promise: it works at room temperature, is small enough to wear on the head and requires less intensive shielding.

OPM-MEG technology is not yet ready for primetime. But it could become an important new BCI modality in the years ahead, offering higher-fidelity brain data than EEG while still avoiding invasive surgery.

A third non-invasive BCI modality worth mentioning is functional near-infrared spectroscopy, or fNIRS.

Instead of measuring electrical activity like EEG does, or magnetic activity like MEG does, fNIRS measures blood flow. Blood flow increases to neurons when they fire because neurons that are firing require more nutrients. By beaming high-wavelength light through the skull and into the brain, fNIRS sensors can detect changes in blood flow and use those patterns to decode brain activity.

fNIRS is today the second most common non-invasive BCI sensor in the world, behind only EEG. This is thanks in large part to the efforts of Bryan Johnson’s startup Kernel over the past decade. Kernel’s key achievement was to miniaturize fNIRS technology, turning it for the first time into a wearable device that could be commercialized at scale. Like EEG, fNIRS is safe, portable and comparatively cheap. fNIRS is more accurate than EEG in terms of location but less accurate than EEG in terms of timing; the two modalities are thus complementary and often used in tandem.

This brings us to today’s buzziest and most promising non-invasive BCI modality of all: focused ultrasound. We will have much more to say about ultrasound in this article. Read on!

The best way to understand the state of the art in non-invasive BCI—what is possible, what is not possible, where the biggest future opportunities lie—is to explore what today’s leading startups are doing. Let’s dive in.

Reading Minds with EEG

A cohort of stealthy startups believes that humble EEG is poised to transform from a familiar but limited sensor into the dominant approach to BCI.

EEG has many advantages. For decades, though, conventional wisdom has held that EEG’s signal quality is simply too poor to support advanced BCI capabilities.

How convenient, then, that one of modern AI’s great strengths is its superhuman ability to extract latent signal from noisy data.

If you are a hardcore deep learning disciple—a “Bitter Lesson” maximalist—there are good reasons for EEG to be your BCI modality of choice. In one word: scale.

The current era of AI has been defined by the principle of scaling. OpenAI popularized the concept of “scaling laws” in 2020: the idea that AI systems predictably improve as training data, model size and compute resources increase. AI’s dramatic advances in the half-decade since then have resulted, more than anything else, from scaling everything up. The reason that large language models are so astonishingly capable is that we figured out how to train them on more or less all the written text that humanity has ever produced.

If one wanted to take the playbook that has worked so well for generative AI and apply it to understanding the human brain, the key would be to collect as much brain training data as possible. And if one wanted to collect as much brain training data as possible, the best sensor to choose would be obvious: EEG. EEG is, put simply, far more scalable than any other BCI modality.

There are several orders of magnitude more EEG systems in the world today than every other kind of BCI sensor combined. EEG devices can be found in most hospitals in the world; by contrast, there are perhaps a few thousand fNIRS systems and a few hundred MEG systems globally. Basic EEG systems are available for under $1,000.

One young startup that exemplifies this AI-first, scaling-first approach to non-invasive BCI is Conduit. Cofounded by one young Oxford researcher and one young Cambridge researcher, Conduit is collecting as much data as possible as quickly as possible in order to train a large foundation model for the brain. The company says it will have collected over 10,000 total hours of brain recordings from several thousand participants by the end of the year.

While Conduit is focused primarily on collecting EEG data, it supplements this with other non-invasive modalities because the company has found that its AI’s performance improves dramatically when trained on multiple sensor modalities from each user rather than just one.

What use case is Conduit envisioning for its technology?

The company’s goal is—astonishingly—to build a BCI product that can decode users’ thoughts before they have even formulated those thoughts into words. In other words, they are seeking to build thought-to-text AI.

And according to the company, the system is already beginning to work. Conduit’s current AI model produces text outputs that achieve ~45% semantic matches with users’ thoughts, and can do so zero-shot (meaning that the AI system is not fine-tuned on any particular individual ahead of time).

A few specific examples will help make this more concrete.

In one example, when a human participant thought the phrase “the room seemed colder,” the AI generated the phrase “there was a breeze even a gentle gust.” In another example, the participant thought “do you have a favorite app or website” and the AI generated “do you have any favorite robot.”

This technology is not yet ready for primetime. 45% accuracy is not good enough for a mass-market product. And, for now, these results are only possible when users put an unwieldy suite of sensors on their heads. But this level of accuracy is nonetheless remarkable when considering that the task at hand is reading people’s minds. And the company is just getting started. Conduit only began scaling its data collection efforts a few months ago; the company plans to increase its training data corpus by several orders of magnitude moving forward.

Imagine what might become possible—imagine how society might change—if it were possible to communicate nuanced ideas to other people and to computers merely by thinking them.

"The biggest lesson from ML in the last decade has been the importance of scale and data,” said Conduit cofounder Rio Popper. “Noninvasive approaches let us collect a much larger and more diverse dataset than we’d be able to if everyone in our dataset had to get brain surgery first.”

Added her cofounder Clem von Stengel: “We founded Conduit because we realized that people could get things done so much faster if we all thought directly in ideas rather than in words. And we could have a much richer understanding of each other and of the world in general.”

Another interesting young startup pushing the limits of what is possible with EEG is Alljoined.

Alljoined, like Conduit, is taking an AI-first approach to non-invasive BCI and is betting on EEG as the right modality given its scalability and accessibility. While Conduit’s goal is to decode thoughts into language, Alljoined’s initial focus is to decode thoughts into images—that is, to faithfully reproduce an image that a user has in his or her “mind’s eye” based on EEG readings, a task known as image reconstruction.

Alljoined’s CEO/cofounder Jonathan Xu co-authored the seminal MindEye2 paper, which showed that generative AI-based methods could achieve accurate image reconstruction based on only modest amounts of fMRI data. Alljoined set out to extend that work from fMRI to EEG data—and has already had success doing so.

The graphics below show some examples of images that Alljoined’s AI system reconstructed from participants’ EEG data. As you can see, the reconstructed outputs are not fully accurate, but these results represent state-of-the-art performance today. And—as we have observed in so many other fields in AI—it is a safe bet that the system’s performance will continue to improve as training data and compute scale.

The top row represents the image that a human participant looked at, and the bottom row represents the image that Alljoined's AI system reconstructed based on the participant's EEG data.

SOURCE: ALLJOINED

Speaking of training data, last year Alljoined open-sourced the first-ever dataset specifically built for image reconstruction from EEG. The dataset contains EEG data from 8 different participants looking at 10,000 images each. Making this data freely available should serve as a helpful catalyst for the entire field.

While Alljoined’s initial focus has been on image reconstruction, the company is also exploring other application areas. One promising area is sentiment analysis—the ability to accurately and granularly identify the emotion that a user is experiencing in real-time. Decoding sentiments directly from brain data could have significant commercial relevance, for instance in marketing and consumer behavior research, and would be far more high-fidelity than the current status quo of asking individuals to self-report their emotions.

One final EEG startup worth mentioning is Israel-based Hemispheric.

Founded by one of the co-creators of Apple’s FaceID technology, Hemispheric is going all in on the pursuit of scaling laws for EEG. The company is establishing EEG data collection facilities around the world, systematizing and modularizing how these facilities are set up in order to scale as quickly as possible.

The company, which plans to come out of stealth mode in the coming months, has spent years developing a novel model architecture to train a state-of-the-art foundation EEG model. The company recently successfully scaled and trained its first multi-billion-parameter model.

“Some companies are focused on developing improved non-invasive sensors, betting that better hardware will unlock high-precision non-invasive BCI products,” said Hemispheric CEO/cofounder Hagai Lalazar. “We are making the opposite bet: that current non-invasive sensing modalities (EEG, MEG, fNIRS) suffice, and that the breakthrough will come not from better sensing but from better decoding of existing signals. AI is the biggest revolution in the history of algorithms, but so far no one has scaled brain activity data collection and model training for decoding neural data. We believe that a breakthrough in developing AI for decoding the ‘language’ of the brain’s electrical activity is the missing link to making non-invasive BCIs pervasive.”

Zooming out, it is important to note that plenty of uncertainty and skepticism still exist as to whether EEG paired with cutting-edge AI will be able to deliver on the lofty visions outlined here. Many observers are doubtful or downright dismissive of the idea that sufficiently high-signal data can ever be extracted from EEG readings to enable advanced BCI use cases. Much skepticism comes in particular from those who focus on invasive approaches to BCI, those who have witnessed and worked with EEG’s limitations first-hand for decades, and/or those who do not come from the world of deep learning. And some recent research has cast doubt on progress in language decoding from EEG.

The skeptics may prove to be right.

The reality, though, is that no one—not the skeptics, not these AI-first EEG startups, not any BCI or AI expert in the world—knows for sure. No one in the world has yet collected EEG training data at massive scale and trained a large neural network on it and assessed its performance. No one has yet definitively validated or falsified the hypothesis that scaling laws exist for EEG foundation models like they do for large language models.

When OpenAI published the first GPT model in 2018, no one could have conceived of, and no one would have believed, the breathtaking performance gains that would result over the next few years from sheer scaling.

Only time will tell whether scaling will prove anywhere near as productive in the world of BCI as it has for LLMs. If it does, don’t sleep on EEG.

Consumer Wearables for Neuromodulation

From FitBit (acquired by Google for $2.1 billion) to ōura (recently valued at $11 billion) to Apple Watch (generating well over $10 billion in annual revenue), a number of consumer wearable products have achieved breakout success in recent years.

What do all these consumer wearable products have in common? They measure your personal health metrics, but they cannot change them. They can only “read”; they cannot “write”. (The EEG use cases discussed above all likewise involve only reading, not writing.)

A new generation of consumer wearable companies is building brain-focused products that don’t just monitor your brain state but actively modulate it. If these products work as expected, it’s not hard to imagine that one of them could become the next ōura.

One intriguing example is Somnee Sleep, a startup that has built a headband to improve the quality of its users’ sleep.

Somnee was co-founded by four of the world’s leading sleep scientists, including UC Berkeley professor Dr. Matthew Walker, author of the influential book Why We Sleep.

No mental activity is more universal or more important than sleep. A consumer product that could significantly improve users’ sleep could unlock a massive market opportunity: as a point of reference, $80 billion is spent on sleeping pills annually.

How does Somnee work?

Somnee’s headband uses EEG and other sensors to track your brain’s activity during sleep, learning its particular sleep patterns and signals using AI. It then sends out personalized electrical pulses that nudge your brainwaves into their optimal rhythms for deeper, more efficient sleep. This neuromodulation technology is known as transcranial electrical stimulation, or tES.

Research shows that Somnee's consumer headband is four times more effective than melatonin and 1.5 times more effective than sleeping pills like Ambien at improving sleep.

SOURCE: SOMNEE SLEEP

Does it actually work?

Peer-reviewed research suggests that it does.

One recent clinical study showed that Somnee’s product is four times more effective than melatonin and 50% more effective than sleeping pills like Ambien at improving sleep efficiency.

In another study that the company recently completed, Somnee’s headband helped users fall asleep twice as fast, stay asleep more than 30 minutes longer and reduce tossing and turning by one-third.

The National Basketball Association recently announced that it is partnering with Somnee to make the company’s product available to NBA players. Equinox will also soon make Somnee’s headbands available in its gyms and hotels.

Another noteworthy startup in this category is UK-based Flow Neuroscience. Similar to Somnee, Flow’s product is a wearable headband that uses transcranial electrical stimulation to generate gentle personalized electrical pulses that modulate its user’s brain activity. But while Somnee focuses on improving sleep, Flow’s product is designed to combat depression.

Depression affects a key region of the brain called the dorsolateral prefrontal cortex. In depressed individuals, the brain cells in this region become less active. Flow’s headband delivers precisely calibrated electrical stimulation directly to the dorsolateral prefrontal cortex in order to stimulate this region and restore healthy brain cell activity patterns.

Both Somnee and Flow rely on transcranial electrical stimulation (tES). But while Somnee uses transcranial alternating current stimulation (tACS), Flow makes use of transcranial direct current stimulation (tDCS). What’s the difference? In short, direct current products like Flow provide a constant current to the brain that make neurons generally more likely to fire, while alternating current products like Somnee introduce an oscillating pulse that influences the rhythms and frequencies at which neurons fire.

Like Somnee, the efficacy of Flow’s product has been validated in peer-reviewed studies. A large clinical trial published last year in Nature Medicine found that the Flow product is twice as effective at addressing depression as antidepressant drugs. According to the study, 57% of clinically depressed patients who used the Flow product reported that they no longer had depression after 10 weeks. The company reports that, of its total user base of tens of thousands of customers, over 75% see some clinical improvement within three weeks.

Flow describes its product as “electricity as medicine.” It is an apt phrase.

Both Somnee and Flow’s headbands are available online to the general public.

One final startup worth mentioning is Neurode. Neurode’s headband uses electrical stimulation to improve its users’ focus and attention. The product is intended both for individuals with ADHD and for members of the broader population looking to boost their overall cognitive functioning.

While Flow uses tDCS (a constant current) and Somnee uses tACS (a rhythmically oscillating current), Neurode uses transcranial random noise stimulation, or tRNS, which delivers current that fluctuates randomly in both its frequency and amplitude. Emerging research suggests that introducing this random noise can boost signal detection in neural circuits, thus improving learning and focus.

According to the company, 45% of its users experience an increase in focus within the first week of using the product.

An emerging body of clinical research indicates that electrical stimulation of the brain with consumer-grade hardware, like these companies are pursuing, can indeed meaningfully influence brain behavior and individual experience in areas as diverse as sleep, depression and focus.

“These startups are building at the right moment,” added Andrea Coravos, a former Entrepreneur in Residence in the FDA’s Digital Health Unit. “The regulatory infrastructure isn’t playing catch-up. The FDA’s first AI/ML framework came out in 2019, and nearly 1,000 AI-enabled devices have been authorized since. That regulatory foundation is what lets companies move from research to real humans, faster.”

But none of these products have yet won mainstream adoption. Time will tell whether these companies are able to craft product experiences that are delightful enough and go-to-market strategies that are effective enough to turn these devices into mass-market successes.

Focused Ultrasound: The Next Great BCI Paradigm?

If there is one BCI technology that offers the greatest upside potential—one approach that could transcend the existing landscape of solutions (including those discussed in this article) and usher in a new paradigm for neurotechnology—it is focused ultrasound. No area within the world of brain-computer interfaces is generating more buzz and excitement today.

What exactly is focused ultrasound, and why is it so promising?

At a basic level, ultrasound is just a subcategory of sound—that is, waves that travel through particles in air and other materials. Humans can hear sound waves that fall within a certain range of frequencies. Ultrasound waves are simply sound waves with a higher frequency than humans can detect with their ears (>20 kilohertz), but that otherwise behave similarly to audible sound waves.

Ultrasound technology has been used for medical imaging for over 75 years (as anyone who has ever been pregnant or had a pregnant loved one will recall).

Focused ultrasound for the brain is a much newer innovation—one that began to take shape only in the 2010s.

The basic concept of focused ultrasound is to aim and launch many ultrasound waves in a precise sequence such that they all converge at one particular point in the brain. All the individual waves add together at that one focal point, creating enough energy density and mechanical pressure to modulate the neurons in particular ways in that one spot while leaving unaffected the rest of the brain tissue that the waves travel through. (The two simple animated graphics on this page do an excellent job of visualizing this phenomenon, making it intuitive to grasp.)

Focused ultrasound offers several unique and compelling advantages as a BCI modality.

The first is that it is orders of magnitude more precise than any other non-invasive BCI modality. EEG, fNIRS and tES all offer spatial resolution of a few centimeters. Focused ultrasound, by contrast, can target a particular spot in the brain with sub-millimeter precision. It can be thought of as a high precision beam that can be aimed at the exact location in the brain that one wants to target.

Second, focused ultrasound can reach deeper into the brain than any other non-invasive technology.

Because non-invasive sensors sit outside the skull, they generally are only able to access and interact with the outermost layer of the brain, known as the neocortex. The neocortex is the seat of advanced cognition and language, so plenty of useful applications are achievable with sensors that can only reach the neocortex. But many important regions and functions sit deeper inside the brain and are therefore out of reach for EEG, fNIRS, tES and other non-invasive sensors.

Deep brain structures like the thalamus, hypothalamus, hippocampus, basal ganglia and amygdala regulate many of our fundamental drives and functions: emotions, memory, attention, appetite, mood, movement, motivation, cravings. The ability to precisely modulate these deep brain regions could enable powerful new treatments for neuropsychiatric disorders as diverse as Parkinson’s, OCD, depression, Alzheimer’s, epilepsy, anxiety, chronic pain and PTSD—not to mention unlocking cognitive augmentation for the general population.

Up until now, access to these deeper regions could only be achieved via invasive methods that require surgery, like deep brain stimulation (DBS). All non-invasive modalities other than ultrasound—whether electrical, magnetic, optical or infrared—are attenuated by human tissue, which means they can make it only a limited distance before they dissipate. Focused ultrasound, by contrast, is a mechanical wave and as a result can pass through human tissue with very little attenuation. This enables it to travel deep into the brain while maintaining its concentrated focus.

And these possibilities are not just theoretical. Recent research has shown that focused ultrasound can, for instance, meaningfully reduce chronic pain in patients; decrease opioid cravings in participants with serious addictions; and minimize tremors for those who suffer from essential tremor—all of which involve accessing deep brain structures.

One final advantage of ultrasound that sets it apart from every other non-invasive modality: it can both read and write, and it can do both with high resolution. No other individual non-invasive modality can carry out both of these functions. EEG, fNIRS and MEG can read, but they cannot write. Transcranial electrical stimulation can write (though at lower resolution and shallower depth than focused ultrasound), but it cannot read.

The ability to both read and write unlocks the holy grail for brain-computer interfaces: closed loop functionality, whereby one unified system can read and decode ongoing neural activity, then stimulate the brain in selective and personalized ways based on what it reads, then see how the brain responds and adapt in realtime, and so on.

Compared to using one device to sense and a different one to modulate, a single device that can both read and write enables perfect alignment between sensing and stimulation, low latency, straightforward calibration, less hardware complexity, greater space efficiency, lower cost, and ultimately more scalable products.

The startup landscape for ultrasound BCI is nascent but developing at breakneck speed.

Today’s most high-profile focused ultrasound startup is Nudge, which recently announced a $100 million fundraise led by Thrive and Greenoaks.

Nudge CEO/cofounder Fred Ehrsam previously cofounded both Coinbase and Paradigm, two of the most successful organizations in the world of crypto. Nudge thus continues the lineage of billionaires launching BCI startups, following Elon Musk with Neuralink, Bryan Johnson with Kernel, and Sam Altman with Merge Labs (more on Merge below). Nudge’s other cofounder Jeremy Barenholtz previously led product and technology at Neuralink.

Nudge’s mission is to advance the state of the art in focused ultrasound across the full stack of hardware, AI and neuroscience in order to enable precise and powerful non-invasive neuromodulation. The company’s initia...