  Recently there has seen promising results on automatic stage scoring by
extracting spatio-temporal features from electroencephalogram (EEG). Such
methods entail laborious manual feature engineering and domain knowledge. In
this study, we propose an adaptive scheme to probabilistically encode, filter
and accumulate the input signals and weight the resultant features by the
half-Gaussian probabilities of signal intensities. The adaptive representations
are subsequently fed into a transformer model to automatically mine the
relevance between features and corresponding stages. Extensive experiments on
the largest public dataset against state-of-the-art methods validate the
effectiveness of our proposed method and reveal promising future directions.
