UNEEG SubQ Receives MR Conditional Labeling in Europe and the United States
COPENHAGEN, DENMARK, JUNE 26, 2026 – UNEEG Medical is pleased to announce that it’s subcutaneous EEG implant, UNEEG SubQ, has received MR conditional labeling in both Europe and the United States. This milestone enables patients with the implant to safely undergo MRI procedures at both 1.5 T and 3 T, without the need for device removal.
With this approval, UNEEG SubQ becomes the first implant of its kind to support MRI scans, marking a significant advancement in epilepsy care and long-term brain monitoring.
“This is an important step for us and for people living with epilepsy,” says Martin Stenfeldt, CEO of UNEEG Medical. “Ensuring that patients can undergo essential diagnostic procedures such as MRI without additional surgical interventions represents meaningful progress toward more integrated and patient-centered care.”
MR conditional labeling eliminates the need for explantation prior to MRI scans, reducing the burden on patients and healthcare systems while enabling a more efficient and less invasive clinical pathway. This advancement supports uninterrupted, ultra long-term EEG monitoring, while preserving access to critical diagnostic imaging.
UNEEG Medical continues to focus on enabling continuous brain monitoring without disruption. By combining long-term EEG data collection with MRI compatibility, the aim is to provide clinicians with deeper real-world insights while simplifying the patient journey.
“Our solution is designed with minimum hassle for the patient, supporting our vision of delivering seamless and accessible brain monitoring,” Martin Stenfeldt adds. “By reducing barriers in epilepsy care, we are helping to make the clinical pathway simpler, safer, and more efficient for both patients and clinicians.”
This development reinforces UNEEG Medical’s commitment to driving innovation in brain health and advancing our mission to improve outcomes for people living with neurological conditions through actionable insights derived from real-world data.