# One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing

arXiv: https://arxiv.org/abs/1809.01926 · HTML: https://mlsanity.com/paper/1809.01926/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2018.

## Built on by (most cited first, 14 of 14)

- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges](https://mlsanity.com/paper/2112.15424/) (2021)
- [Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection](https://mlsanity.com/paper/2105.00934/) (2021)
- [Learning from Hypervectors: A Survey on Hypervector Encoding](https://mlsanity.com/paper/2308.00685/) (2023)
- [Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection](https://mlsanity.com/paper/2111.08463/) (2021)
- [HEAL: Brain-inspired Hyperdimensional Efficient Active Learning](https://mlsanity.com/paper/2402.11223/) (2024)
- [Shorter Latency of Real-time Epileptic Seizure Detection via Probabilistic Prediction](https://mlsanity.com/paper/2301.03465/) (2023)
- [Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Service](https://mlsanity.com/paper/2310.06840/) (2023)
- [Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data](https://mlsanity.com/paper/2402.17572/) (2024)
- [EnHDC: Ensemble Learning for Brain-Inspired Hyperdimensional Computing](https://mlsanity.com/paper/2203.13542/) (2022)
- [Modeling and Predicting Transistor Aging under Workload Dependency using Machine Learning](https://mlsanity.com/paper/2207.04134/) (2022)
- [NasHD: Efficient ViT Architecture Performance Ranking using Hyperdimensional Computing](https://mlsanity.com/paper/2209.11356/) (2022)
- [Robust Clustering using Hyperdimensional Computing](https://mlsanity.com/paper/2312.02407/) (2023)
- [Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challenges](https://mlsanity.com/paper/2503.19949/) (2025)
- [A Composable Channel-Adaptive Architecture for Seizure Classification](https://mlsanity.com/paper/2512.19123/) (2025)

## Abstract (arXiv)

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.