@CoganLab

Cogan Lab at Duke University: Investigating speech, language, and cognition using invasive neural human electrophysiology

 Durham NC
coganlab.org
Joined July 2019
  • Tweets716
  • Following512
  • Followers918
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Cogan Lab retweeted
Gregory B. Cogan@Cogan_GSep 26
Come be our colleague!!
Duke Institute for Brain Sciences@DukeBrainSep 26
📢 To everyone on the job market who has a vision for neuroscience applied to biomedical applications & technological innovation: The Duke Dept. of Biomedical Engineering announces a tenure-track faculty job search in neural engineering, broadly defined! academicjobsonline.org/ajo/j…
View of Duke Chapel looking up a quiet street surrounded by colorful autumn foliage

ALT View of Duke Chapel looking up a quiet street surrounded by colorful autumn foliage

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Congratulations @bentang0 and @zspald11 on your fantastic preprint! An exciting start for the lab into unsupervised learning and foundation models for speech and language decoding. @Duke_Neurology @DukeBrain @DukeEngineering
Ben Tang
@bentang0Sep 17
A new state-of-the-art neural foundation model. Excited to announce Pretraining for Sample-Efficient Neural Interfaces, a simple pretraining method for intracranial brain-computer interfaces! (1/n)
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Can a speech BCI learn across people? In Nature Communications, led by @zspald11: High-density µECoG reveals shared neural dynamics that can be aligned to improve cross-patient speech decoding, even with little data from a new participant. doi.org/10.1038/s41467-026-7… 1/5

Shared latent representations of speech production for cross-patient speech decoding

Nature Communications - Current speech brain-computer interfaces (BCIs) rely on patient-specific decoding approaches. Here, the authors show that patient-specific data can be aligned to a shared...

nature.com
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Two plots testing how neural-interface sampling affects cross-patient speech decoding. Decoding improves as electrode spacing becomes denser and as cortical grid coverage becomes broader. Aligned cross-patient models outperform patient-specific models at the highest electrode densities and broadest coverage, showing that both dense and broad sampling help recover shared neural structure.

ALT Two plots testing how neural-interface sampling affects cross-patient speech decoding. Decoding improves as electrode spacing becomes denser and as cortical grid coverage becomes broader. Aligned cross-patient models outperform patient-specific models at the highest electrode densities and broadest coverage, showing that both dense and broad sampling help recover shared neural structure.

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5/5 Thank you to all the patients who participated in our work, our colleagues in the Viventi lab, and our neurosurgeon collaborators! @LadNeurosurgery @rshervin @dsuseendar @Duke_Neurology @Dukeneurosurg @DukeBrain @DukeUBME @DukeMedSchool
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How does a speech plan become fluent movement? In Nature Human Behaviour, led by @dsuseendar: Intracranial recordings reveal how hierarchical speech plans become continuous motor sequences across planning, articulation, and monitoring networks. doi.org/10.1038/s41562-026-0… 1/5

Distinct neural processes link speech planning and execution

Nature Human Behaviour - Direct recordings from the human brain reveal a hierarchy in planning to speak, with neural activity organizing whole syllables before the individual sounds that compose...

nature.com
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A big thank you to all the patients for participating in our research, all our co-authors including the Viventi Lab, and our neurosurgical team @rshervin @LadNeurosurgery, @GregoryHickok @Duke_Neurology @Dukeneurosurg @DukeBrain @DukeUBME @DukePsychNeuro @DukeMedSchool 5/5
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Nature Human Behaviour also published a Research Briefing on the work, with a short overview of the findings, check it out! nature.com/articles/s41562-0…

How speech plans become spoken sequences

Nature Human Behaviour - Speaking requires the brain to rapidly convert planned speech into precisely ordered vocal movements. Intracranial recordings reveal a cascade of neural processes in which...

nature.com
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Two new papers from the lab in Nature Human Behaviour and Nature Communications, spanning the neural mechanisms of speech production and new approaches to speech neurotechnology! 🧠 Speech plans → fluent motor sequences 🗣️ Speech BCIs across people Links below 👇 1/3
Graphic announcing two new Cogan Lab papers. The Nature Human Behaviour paper asks how the brain turns a speech plan into fluent movement, illustrated as planning → articulation → monitoring. The Nature Communications paper asks whether a speech BCI can learn from other people, illustrated by neural activity from two individuals converging on a shared latent representation.

ALT Graphic announcing two new Cogan Lab papers. The Nature Human Behaviour paper asks how the brain turns a speech plan into fluent movement, illustrated as planning → articulation → monitoring. The Nature Communications paper asks whether a speech BCI can learn from other people, illustrated by neural activity from two individuals converging on a shared latent representation.

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Distinct neural processes link speech planning and execution Nature Human Behaviour doi.org/10.1038/s41562-026-0… 2/3

Distinct neural processes link speech planning and execution

Nature Human Behaviour - Direct recordings from the human brain reveal a hierarchy in planning to speak, with neural activity organizing whole syllables before the individual sounds that compose...

nature.com
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Shared latent representations of speech production for cross-patient speech decoding Nature Communications doi.org/10.1038/s41467-026-7… 3/3

Shared latent representations of speech production for cross-patient speech decoding

Nature Communications - Current speech brain-computer interfaces (BCIs) rely on patient-specific decoding approaches. Here, the authors show that patient-specific data can be aligned to a shared...

nature.com
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Last week, Areti Majumdar (2nd year PhD student) presented @jerryptang and colleague’s seminal paper on reconstructing continuous language from non-invasive brain recordings. This 🧵 explores our thoughts and questions. nature.com/articles/s41593-0…

Semantic reconstruction of continuous language from non-invasive brain recordings

Nature Neuroscience - Tang et al. show that continuous language can be decoded from functional MRI recordings to recover the meaning of perceived and imagined speech stimuli and silent videos and...

nature.com
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❔3⃣: The decoder was trained on speech perception and successfully generalized to imagined speech, suggesting that it captures a shared semantic representation across tasks. How well would you expect this type of model to generalize to overt speech production?
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Thank you to the authors @UCBerkeley/@UTAustin for your work, and we look forward to following its progress! cc: @shaileejain @amandalebel3 @alex_ander
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