Byte-Pair Encoding for classifying routine clinical electroencephalograms in adults over the lifespan

Our study considered the problem of classifying routine clinical EEG. We incorporated NLP tools into the workflow for time series classification. We transformed EEG signals into strings of symbols. We then applied byte-pair encoding (BPE) to split the new text into combinations of symbols or tokens, each associated with different patterns of changes in EEG amplitude. We validated the proposed workflow under the framework of predicting patients' biological age.