Metaが外科手術不要で脳活動からのテキスト読み取りを可能とするAIモデル「Brain2Qwerty v2」を開発しました。Brain2Qwerty v2は外科手術を伴う侵襲的な手法と同等のテキスト予測精度を達成しているとのことです。

Modern neuroprostheses can now restore communication in patients who have lost the ability to speak or move. However, implanting these invasive devices comes with risks inherent to neurosurgery. Here we introduce a noninvasive method to decode the production of sentences from brain activity and demonstrate its efficacy in a cohort of 35 healthy volunteers. For this, we present Brain2Qwerty, a new deep learning architecture trained to decode sentences from either electro- or magnetoencephalography, while participants typed briefly memorized sentences on a QWERTY keyboard. With magnetoencephalography, Brain2Qwerty reaches, on average, a character error rate of 29% and substantially outperforms electroencephalography (character error rate: 65%). For the best participants, the model achieves a character error rate of 18%, and can perfectly decode a variety of sentences outside of the training set. Overall, these results narrow the gap between invasive and noninvasive methods and thus open

Metas KI-Modell Brain2Qwerty rekonstruiert getippte Sätze aus EEG- und MEG-Hirnströmen – ohne Implantate. Die KI erreicht im Schnitt 61 Prozent Wortgenauigkeit.

Here the authors introduce Brain2Qwerty, a deep learning model that decodes typed sentences from non-invasive brain activity with character error rate down to 18%. This opens a pathway for non-invasiv...

Meta AI Introduces Brain2Qwerty: A New Deep Learning Model for Decoding Sentences from Brain Activity with EEG and MEG

Metaは、新しい深層学習アーキテクチャ「Brain2Qwerty」を発表した。人間がQWERTYキーボードで文章を入力している際の脳波または脳磁図から文章を解読する技術だ。

Last year, we introduced Brain2Qwerty v1, research that uses AI to decode brain activity into text without any surgical implant. Now we're sharing the next step: Brain2Qwerty v2, the…

Metaが外科手術不要で脳活動からのテキスト読み取りを可能とするAIモデル「Brain2Qwerty v2」を開発しました。Brain2Qwerty v2は外科手術を伴う侵襲的な手法と同等のテキスト予測精度を達成しているとのことです。

Die Unstatistik des Monats Februar ist die Berichterstattung zahlreicher Technologie-Medien über eine Studie zur Vorhersagegenauigkeit der Deep-Learning-Architektur Brain2Qwerty.

Unlock the power of brain activity with Meta's Brain2Qwerty. Read about the technology that enables communication for those with speech loss.

Brain2Qwerty is Meta's non-invasive AI system that decodes typed sentences from brain activity, hinting at new tools for people who cannot speak or type.

A non-invasive brain-computer interface that decodes typed sentences from magnetoencephalography (MEG). Work by Meta AI in collaboration with BCBL.

A deep dive into the most exciting research yet, turning brainwaves into text

Metaが外科手術不要で脳活動からのテキスト読み取りを可能とするAIモデル「Brain2Qwerty v2」を開発しました。Brain2Qwerty v2は外科手術を伴う侵襲的な手法と同等のテキスト予測精度を達成しているとのことです。

Meta's Brain2Qwerty v2 pushes non-invasive brain decoding forward, but hardware, speed, and real-world usability remain the biggest hurdles.

Meta FAIR's Brain2Qwerty v2 decodes typed sentences from brain activity using a non-invasive MEG scanner, no implant. A real leap for non-invasive brain-to-text (~8% to ~61% word accuracy), but with honest limits: a 39% word error rate and a room-sized lab scanner. Aimed at assis

Meta's Brain2Qwerty v2 decodes typed sentences from brain scans at 61% accuracy with no implant. The catch: it's room-sized, not real-time, and lab-bound. @tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

Meta's unveils Brain2Qwerty v2, non-invasive AI that decodes typed sentences from brain waves using MEG scanners, hitting 61% word accuracy without implants.