Synaptrix | Advancing the Future of Neural Interfaces
Introducing Atlas™ by Synaptrix, the first device that allows you to use your computer, wheelchair, prosthetics, and to communicate through brain activity alone.
https://www.synaptrix-labs.com/?ref=land-book.comReader - page text saved at the timeAdvancing the frontier of neural interfaces to improve the human condition.
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The brain exploration company
Synaptrix is building the first vertically integrated neuroAI platform — combining high-density non-invasive hardware, large-scale neural datasets, and novel AI models specifically for brain signals.
At Synaptrix, we are creating new kinds of AI to push the frontier of non-invasive brain-computer interfaces. We believe that within this decade, our systems will reach capabilities comparable to implants.
TECHNOLOGY
Generative Neuroscience
NeuroDiffusion
Cortex
Portal
Platform
Generative Neuroscience™
Generative Neuroscience is our research framework for reconstructing high-resolution neural representations from non-invasive EEG recordings. By learning the representations underlying cortical activity, our models are able to understand true neural intent non-invasively.
Software Product
NeuroDiffusion™
NeuroDiffusion is a real time, deep learning architecture that treats EEG as a high dimensional dynamical system to be modeled, reconstructed, and aligned at scale. It's primary purpose is to clean non-invasive EEG recordings.
Neural Data Engine
Cortex™
Cortex is our continuously expanding neural data engine, a unified brain data lake that aggregates and structures decades of published electrophysiology datasets alongside real time streams from our deployed devices.
Hardware
Portal™
Our next generation neural interface designed to record brain activity from the motor cortex using non-invasive EEG. Built for continuous use, Portal™ combines high density neural sensing with a lightweight wireless architecture that allows natural movement while maintaining clinical grade signal stability.
Advancing Brain Interfaces Through Clinical Research
Millions live with mobility and communication impairments without access to effective assistive technologies.
CLINICAL STUDIES
EMPOWER
Evaluating real-time brain-controlled mobility.
EMPOWER is a clinical study assessing a non-invasive EEG-based system that enables participants to control a powered wheelchair using neural signals.
ELIGIBILITY:
Be 18 years or older
Have a diagnosis such as quadriplegia, paraplegia, high-level spinal cord injury, ALS, cerebral palsy, advanced MS, stroke with motor impairment, limb loss, or comparable condition
Be medically stable and suitable for placement of a non-invasive EEG cap
Be able to tolerate upright seated sessions
Apply to EMPOWER
EEG RECORDING STUDY
Help us build better brain models.
We are conducting an EEG recording study open to individuals interested in contributing neural data to help improve our AI systems.
Open to healthy individuals and those interested in contributing neural data.
Participants wear a non-invasive EEG cap during guided cognitive or motor tasks.
Data collected supports the training of large-scale neural foundation models.
Healthy volunteers welcome
Non-invasive
Short study sessions
Contribute to research
Sign Up for Recording Study
WHAT IS SYNAPTRIX
We believe that one day, there will be a single device that can diagnose and cure most neurological disorders, if not all disease altogether.
Understanding the brain is the key to this future
For nearly a century, EEG has given us a non-invasive window into the brain. But its limitations: poor spatial resolution, low bandwidth, and noisy signals, have largely been treated as fundamental constraints of the technology. At Synaptrix, we see them as engineering problems. We are rethinking EEG from first principles, combining new approaches to signal acquisition with advanced AI models to recover more information from the brain than was previously thought possible. The goal is to push a century-old technology far beyond the limits people assumed it had.
Open roles
Research
Internship Program
New York City
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Research Scientist, Machine Learning (PhD)
New York City
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