Peng, a serial entrepreneur in China, is pursuing brain–computer interface development through two ventures: NeuroXess, which develops implantable BCI systems, while Gestala is dedicated to non-invasive interfaces that rely on ultrasound technology. The efforts come as U.S. companies including Elon Musk’s Neuralink and OpenAI-backed Merge Labs continue advancing similar technologies.

Investor demand significantly exceeded the amount sought, with commitments totaling more than $58 million, Peng said.

The deal marks the biggest early-stage funding round so far in China’s brain–computer interface industry. The capital will help grow the team from 15 to about 35 employees by the end of the year, support R&D, and fund the construction of a manufacturing facility in China. Gestala  expects to complete its first-generation prototype later this year.

Globally, investment in BCI technologies has been rising, especially when it comes to ultrasound technology. This is the first ultrasound BCI company in China.

According to Peng ultrasound may become the next generation of brain–computer interface technology, offering the possibility of broader access to brain activity and new ways to interact with neural signals. Its possible that the non-invasive ultrasound could reduce the risks associated with brain surgery. The ultrasound technology can monitor larger areas of the brain, including deeper neural circuits. Also using phased-array ultrasound, the system can precisely stimulate or inhibit neural activity without requiring surgical procedures, he explained.

The company is also investigating potential uses in mental health conditions such as depression, autism and obsessive-compulsive disorder, PTSD, and even stroke rehabilitation. Some of the longer-term targets include Alzheimer’s disease, essential tremor and Parkinson’s disease.

Gestala is collaborating with major Chinese hospitals to fast-track clinical trials at substantially reduced costs, estimated at just 20% to 33% of comparable research in the U.S. or Europe. The company is also building  “Ultrasound Brain Bank” — a large clinical dataset aimed at training AI models to decode brain activity and aid future neurological diagnostics.

Despite increasing geopolitical tensions, Peng said he hopes researchers in the U.S. and China will collaborate on deep-technology development.

Both countries bring different strengths,

Peng said.

China offers large-scale clinical research capacity and efficient supply chains, while the U.S. has world-class scientific talent.

He added that joint initiatives could also focus on developing large clinical datasets to advance global neuroscience research.

Image: Phoenix Peng LinkedIn profile, Edited – 12.03.2026

Published today, the paper studied the progression of lung opacities in chest CT images of COVID patients and extracted insights about the temporal relationships between CT features and lab measurements. A better understanding of the progression of lung opacities in COVID patients could help inform clinical decisions in patients with pneumonia and yield insights during clinical trials for therapies to treat the virus.

Тhe researchers used an NVIDIA Clara AI segmentation model to automate the time-consuming task of segmenting the total lung in each CT scan. Expert radiologists reviewed the total lung segmentation and manually segmented the lung opacities.

To track the progression of the disease, the researchers used generalized temporal curves, which correlated the CT imaging data with lab measurements. Then they used 3D visualizations to reconstruct the evolution of COVID opacities in one of the patients.

Photo Credits: NVIDIA Developer, News Center

What they found was that lung opacities appeared between one and five days before symptom onset, and peaked a day after symptoms began. They also analyzed two opacity subtypes — ground-glass opacity and consolidation — the ground glass opacities appeared earlier in the disease and persisted for a time after the resolution of the consolidation.

In the paper, the researchers showed how CT dynamic curves could be used as a clinical reference tool for mild COVID-19 cases and might help spot cases that grow more severe over time. These curves could also assist clinicians in identifying chronic lung effects.

The deep learning models were developed using the NVIDIA Clara application framework for medical imaging.