BCIwiki — Brain-computer interface news, research and industry database

The study presents DSNet, a deep denoising structure-preserving neural encoding network that classifies privacy-preserving encoded EEG without decryption. Common spatial pattern (CSP) features are converted to irreversible neural codes — an irreversible neural transformation designed for privacy rather than a formal cryptographic guarantee — and two deep-learning architectures (a feedforward network and a recurrent RNN) classify in the encoded feature space. On public datasets, DSNet-NN exceeded 87% accuracy for every subject, outperforming the RNN variant and baselines while staying resilient to simulated privacy attacks; the study also integrated GPT-4 to generate clinical-style summaries from model outputs.

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version performing best, a sign that brain-signal AI does not scale the way language models do; the results have not been peer-reviewed.