Neural Data Bottleneck Breaking with 1,000-Person EEG Sync, But Wet Lab Challenges Remain

The biggest bottleneck in the brain-computer interface field over the past decade hasn't been decoding algorithms, but the scale and quality of training data. The thousand-person synchronized EEG acquisition technology released on July 22 increased the volume of simultaneous EEG signals obtainable per experiment by three orders of magnitude—this isn't just an engineering metric; it directly changes the training paradigm for neural large models. I've long followed AI applications in biomedical signals, and this breakthrough reminds me of the leap in protein structure prediction from single sequences to multiple sequence alignment: when the data dimension expands from "individual" to "population," what the model learns is no longer noise, but the universal encoding of biological signals.