French neurotech company BIOSerenity ran three months of large-scale EEG AI training experiments on Jean Zay, France's most powerful supercomputer for research, including a 300-million-parameter model trained on 110,000 hours of EEG data across 64 GPUs. It also fully trained two model families, Mercury and Neptune, in three sizes each, from about 13 million to 100 million parameters and on about 5,000 to 40,000 hours of data. Neptune improved as it scaled up while Mercury did the opposite, its smallest version performing best, a sign that brain-signal AI does not scale the way language models do; the results have not been peer-reviewed.
French medtech company BIOSerenity has built an EEG foundation model, BIOSerenity-E1, pre-trained self-supervised on more than 4,000 hours of recordings, and reports strong results on four clinical tasks: normal/abnormal classification, Alzheimer's disease detection, pediatric sleep staging and seizure detection. The normal/abnormal algorithm is already built into a CE-marked medical device, and the seizure-detection algorithm is in clinical evaluation. The work will be shown as a poster at the 8th Journées de Neurophysiologie Clinique in Grenoble, France.
