Who We Are
Σ 3SP – also Sig3 – is devoted to strengthening the clinical imaging communities’ significant MEG and EEG signals. It is dedicated to improve the signal to noise ratio of the raw data.
What We’re Up To
Sig3 has developed Denoise2-EEG™ – a software that strengthens the significant signals for EEG users. Let us show what it can do for you and your patients!
Better Analysis For Better care
When compared to traditional filtering, DN2-EEG has:
- Higher EEG signal strength;
- Confidence volume tightens;
- And detecting the seizure source more accurately!
What is Denoise2-EEG?
DN2-EEG has been designed only to suppress any interfering signals in the data that do not originate from the brain.
- One of the unique features of DN2-EEG is that it employs a spatiotemporal projection method similar to the tSSS MEG algorithm.
- That tSSS algorithm has become an MEG Gold Standard.
- It is widely and routinely used in clinical MEG settings.
- The spatiotemporal algorithm for the data facilitates separation of the brain signals (information from the brain in green) and outside interference (noise or artifacts represented in red).
- DN2-EEG’s spatiotemporal interference suppression method has a performance similar to tSSS, but with fewer strict requirements on the model.
- DN2-EEG does not employ any traditional filters.
The MEG Gold Standard and DN2-EEG’s have the same goals:
- To be automatic – a “one touch” capability and
- To make it objectively and clinically applicable.
Denoise2-EEG™ In Action
Before Interictal Raw Data
After Interictal DN2-EEG
In Summary
EEG analysis often suffers from artifacts, masking the brain activity. Sig3 developed Denoise2-EEG™ software –
- Not based on subjective or user-dependent decisions.
- Employed a scientific-based algorithm similar to one used widely, routinely, and automatically in clinical MEG settings.
- Based on the spatiotemporal interference suppression method with a performance similar to that MEG method.
Using “Denoise2-EEG noise suppressed” vs. traditionally filtered data –
- Researchers and clinicians can get more information from your EEG datasets.
- EEG SNR is significantly stronger.
- That means clinicians can detect a more robust estimation of the epilepsy seizure source location.
- Minimize the need of traditional filtering.
Sig3 is looking into signal orientation and elimination of muscle artifacts. The goal is to minimize the use of traditional EEG filters!
References
- Taulu, S., and Larson, E. (2020). Unified expression of the quasi-static electromagnetic field: Demonstration with MEG and EEG signals. IEEE Transactions on Biomedical Engineering 1–1.
We have over 50 combined years of experience in the MEG, EEG, and signal processing industries one of whom developed the MEG Golden Standard for noise suppression. Now Sig3 wants to employ the same, scientific advances for EEG users.
Meet The Team
Chief Executive Officer
Mr. Otto has over 25 years in the medical imaging and neuroimaging industry with a worldwide network of research and clinical imaging in the neurological and radiological marketplaces.
Software Consultant
Dr. Larson works in MEG and EEG analysis at the University of Washington as a Research Scientist.
Technology Consultant
Dr. Taulu is an Associate Professor of Physics at the University of Washington and director of the I-LABS MEG Brain Imaging Center.
NEED MORE information?
LET’S TALK!
We can assist and improve the medical imaging community.
Let us know how we can help you!