New brain-computer interface AI model improves real-world EEG data while advancing Zyphra's mission to develop human-aligned superintelligence.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
We present histograms depicting distribution of cluster sizes in all the datasets (see Fig. 7-11). Please, note that all the figures are in log-log scale. We see a significant drop in the number of clusters starting from the size of around 100. This drop is present both in DCLM and FineWeb-Edu2 (see Fig. 8 and 9 respectively), and most likely is explained by a combination of the deduplication strategy and quality when creating both datasets: DCLM deduplication was done individually within 10 shards, while FineWeb-Edu2 was deduplicated within every Common Crawl snapshot. We find that large clusters usually contain low quality material (repeated advertisements, license agreements templates, etc), so it’s not surprising that such documents were removed. Notably, DCLM still contained one cluster with the size close to 1 million documents, containing low quality documents seemingly coming from the advertisements (see Appendix).We find both Zyda-1and Dolma-CC contain a small amount of duplicates, which is expected, since both datasets were deduplicated globally by their authors. Remaining duplicates are likely false negatives from the initial deduplication procedure. Note, that distribution of duplicates clusters sizes of these two datasets (Fig. 10 and 11) don’t contain any sharp drops, but rather hyper exponentially decreases with cluster size.
Below is an example of the document from the largest cluster (~1M documents) of duplicates in DCLM (quality score 0.482627):
Is safe? Is scam?
Is safe for your PC?
Is safe or is it scam?
Domain is SafeSafe score: 1
The higher the number, the more dangerous the website.Any number higher than 1 means DANGER.
Positive votes:
Negative votes:
Vote Up Vote Down review
Have you had bad experience with Warn us, please!
Below one will find a few documents with different quality scores from DCLM coming from the same duplicates cluster. Quality score varies from ~0.2 to ~0.04.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
Reported scores underlined.
Pass@1 scores with greedy sampling.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Pass@1 scores with greedy sampling. Livebench 2024-11-25.
Bold: Best score at 1.5B scale w/ greedy sampling
*reported scores
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
Evals (reported underlined). All numbers pass@1 estimated using n=16
Footnote: Training on the Eurus-2-RL dataset did not match the DeepScaleR math evaluation numbers, possibly due to lower quality synthetic math questions in NuminaMath-CoT providing a mixed training signal, or the solvability filtering process with QwQ-preview reducing the difficulty of the dataset. Additionally, the relatively small percentage of code (5%) likely led to math dominating training at the expense of code performance. Training on domain specific datasets and merging resulting models seems to be a potential way to counteract this problem, as demonstrated with SFT in Light-R1.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
San Francisco, California — Zyphra today announced the release of ZUNA, the company’s first foundation model trained on brain data. ZUNA significantly improves the quality and usability of electroencephalography (EEG) data while establishing an early technical foundation for thought-to-text, the direct communication between human thought and AI systems enabled by brain–computer interfaces (BCIs).
ZUNA is a 380M-parameter diffusion autoencoder model that delivers immediate value for EEG practitioners. While scalp EEG data is widely available and noninvasive, it’s often messy and incomplete. ZUNA reconstructs high-fidelity brain signals from imperfect data, improving diagnostics, research workflows, and BCI applications. It also predicts missing channels from sparse inputs and electrode coordinates, scaling seamlessly from consumer headsets to 256-electrode research systems.
“We believe the next major modality in AI beyond language, audio, and vision will be thought-to-text enabled by noninvasive BCIs,” said Paul White, Chief Business Officer of Zyphra. “ZUNA is a step toward that vision, and it solves everyday problems EEG practitioners face today. We are releasing ZUNA open source and want to collaborate with the community to deliver value today while continuing to innovate.”
ZUNA is designed to work across a wide range of EEG systems, from fewer-channel consumer headsets to high-density clinical equipment. It adapts to different sensor layouts and recording conditions. This flexibility allows it to be easily deployed across industries including medical devices, neuroscience research, digital health, and consumer neurotechnology.
ZUNA learned the shared structure of brain signals across a wide range of devices, sensor layouts, and recording conditions using deep learning techniques applied to a diverse set of real-world EEG data. This approach allows ZUNA to generalize beyond any specific electrode configuration, even with incomplete or noisy data. As a result, ZUNA consistently outperforms spherical-spline interpolation, the industry-standard method implemented in MNE, particularly at higher scaling factors where traditional interpolation begins to break down.
ZUNA’s capabilities and performance make it a valuable tool for EEG practitioners today, while delivering a strong foundation for future thought-to-text models that will interpret and decode human thoughts via noninvasive BCIs.
ZUNA is released as open-source software under a permissive Apache 2.0 license, enabling immediate adoption and integration by researchers, clinicians, and organizations worldwide.
Organizations and researchers interested in collaborating with Zyphra to improve future versions of ZUNA for specific use cases are encouraged to reach out to bci@zyphra.com.
For more information please reference the technical paper and the Zyphra blog post or visit www.zyphra.com.
Zyphra is an open-source superintelligence company based in San Francisco, CA on a mission to build human-aligned AI that helps individuals and organizations reach their fullest potential.
