Replay with Stochastic Neural Transformation for Online Continual EEG Classification.

Brain computer interface (BCI) systems used for clinical assistance purposes such as wheelchair control require decoding of streaming brain signals i.e. electroencephalography (EEG) signals over a long period of time with subject shift in the middle. Numerous challenges arise during this online continual brain signal decoding process: 1) the EEG decoder needs to deal with streaming EEG signals from sequentially arriving subjects, with no data available beforehand for large-scale pretraining; 2) the EEG decoder should avoid catastrophic forgetting on previous subjects after learning on a new subject; 3) the EEG decoder should perform well on noisy signals with high variance across subjects. We proposed a principled replay-based approach for this general decoding scenario, forming a bi-level optimization framework with stochastic neural transformation for dynamic memory evolution, making them representative in feature space and encouraging the model to generalize well. The evolved signal segments are stored and replayed during later decoding stages to achieve optimal model performance on all previous subjects. The stochastic neural transformation performed in inner sup of bi-level optimization significantly enhances the diversity of stored signal segments and improves model robustness during online continual decoding. We perform detailed theoretical analysis on model’s generalization ability in addition to the empirical evaluations. We construct multiple new benchmarks to mimic real-world online sequential EEG decoding scenarios with underlying subject shifts. The extensive evaluation of the proposed approach shows it outperforms related strong baselines by a large margin.