nubrain is developing a foundation model for neural decoding, aiming to translate brain activity into meaningful signals for applications in neuroscience and brain-computer interfaces. The company's core technology leverages large-scale machine learning to interpret neural data, potentially enabling new forms of communication and control for users with motor impairments. This approach focuses on building a generalizable model that can be adapted across different neural recording modalities and use cases.
- Artificial Intelligence
- Hardware
- Healthcare Technology
- Medical Devices
- Software Only
Funding
Founders
Product
Problem
Neural decoding—the process of interpreting brain signals to understand intent or control external devices—has traditionally relied on task-specific, bespoke models that require extensive calibration for each individual user and recording setup. This limits the scalability, accessibility, and real-world applicability of brain-computer interfaces (BCIs) and neuroscience research tools.
Solution
nubrain provides a foundation model for neural decoding, offering a generalizable, pre-trained framework that can be fine-tuned for diverse neural data streams and applications. By learning shared representations across large-scale neural datasets, the model reduces the need for per-user or per-task training from scratch, accelerating the development of BCIs and neuroprosthetics. The platform is designed to handle various input types, including spike trains and local field potentials, and to support downstream tasks such as cursor control, speech synthesis, or motor intention prediction. This approach aims to standardize and streamline neural decoding workflows for researchers and device developers.
Target Audience
Primary customers are neuroscience research labs, BCI startups, and medical device companies developing neural interfaces for clinical or assistive applications.
Features
- Pre-trained foundation model architecture optimized for neural time-series data
- Fine-tuning capabilities for custom datasets and specific decoding tasks
- Support for multiple neural recording modalities, including spike and LFP signals
- API-based access for integration into research pipelines and BCI product development
- Scalable inference for real-time or near-real-time decoding applications