Electroencephalography (EEG) is recorded continuously over hours, with relevant dynamics spanning timescales from milliseconds to hours.
Most EEG foundation models nevertheless process fixed windows independently, limiting their ability to capture information encoded in long-timescale dynamics.
State-space architectures enable persistent recurrent processing, but long-range information remains implicitly compressed in recurrent states.
We present NeurDuo-EEG
NeurDuo-EEG introduces multi-timescale memory management with learned consolidation and selective retrieval, enabling persistent modelling of continuous EEG with fixed-size state.
It is pre-trained on 3,955 hours of EEG from 17 public datasets using multichannel autoregressive prediction of discrete spectral codes.
Performance on Downstream Tasks
Across three short-window and two long-sequence downstream tasks, NeurDuo-EEG achieves the best performance on four of five benchmarks, including all three short-window tasks and seizure detection, where AUC-PR improves from $0.285$ to $0.471$ over the strongest non-NeurDuo baseline.
NeurDuo-EEG also remains competitive on sleep staging and supports efficient streaming inference, with nearly constant per-chunk latency as the available history grows to one hour.
Notably, the Small variant achieves this with only 4.7M backbone parameters.
Conclusion
These results demonstrate the value of persistent, multi-timescale modelling for both long-sequence and short-window EEG analysis.
Our code is available at https://github.com/YifaNNW/NeurDuo-EEG.