  This paper presents an efficient binarized algorithm for both learning and
classification of human epileptic seizures from intracranial
electroencephalography (iEEG). The algorithm combines local binary patterns
with brain-inspired hyperdimensional computing to enable end-to-end learning
and inference with binary operations. The algorithm first transforms iEEG time
series from each electrode into local binary pattern codes. Then atomic
high-dimensional binary vectors are used to construct composite representations
of seizures across all electrodes. For the majority of our patients (10 out of
16), the algorithm quickly learns from one or two seizures (i.e., one-/few-shot
learning) and perfectly generalizes on 27 further seizures. For other patients,
the algorithm requires three to six seizures for learning. Overall, our
algorithm surpasses the state-of-the-art methods for detecting 65 novel
seizures with higher specificity and sensitivity, and lower memory footprint.
