# Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure Detection

arXiv: https://arxiv.org/abs/2201.09759 · HTML: https://mlsanity.com/paper/2201.09759/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 24 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Objective evaluation metrics for automatic classification of EEG events](https://mlsanity.com/paper/1712.10107/) (2017)
- [Systematic Assessment of Hyperdimensional Computing for Epileptic Seizure Detection](https://mlsanity.com/paper/2105.00934/) (2021)
- [Interpreting Deep Learning Models for Epileptic Seizure Detection on EEG signals](https://mlsanity.com/paper/2012.11933/) (2020)
- [Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection](https://mlsanity.com/paper/2111.08463/) (2021)

## Built on by (most cited first, 4 of 4)

- [HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration](https://mlsanity.com/paper/2206.04746/) (2022)
- [Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection](https://mlsanity.com/paper/2205.07654/) (2022)
- [Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing](https://mlsanity.com/paper/2303.14745/) (2023)
- [Personalization on a Budget: Minimally-Labeled Continual Learning for Resource-Efficient Seizure Detection](https://mlsanity.com/paper/2509.13974/) (2025)

## Abstract (arXiv)

Wearable and unobtrusive monitoring and prediction of epileptic seizures has the potential to significantly increase the life quality of patients, but is still an unreached goal due to challenges of real-time detection and wearable devices design. Hyperdimensional (HD) computing has evolved in recent years as a new promising machine learning approach, especially when talking about wearable applications. But in the case of epilepsy detection, standard HD computing is not performing at the level of other state-of-the-art algorithms. This could be due to the inherent complexity of the seizures and their signatures in different biosignals, such as the electroencephalogram (EEG), the highly personalized nature, and the disbalance of seizure and non-seizure instances. In the literature, different strategies for improved learning of HD computing have been proposed, such as iterative (multi-pass) learning, multi-centroid learning and learning with sample weight ("OnlineHD"). Yet, most of them have not been tested on the challenging task of epileptic seizure detection, and it stays unclear whether they can increase the HD computing performance to the level of the current state-of-the-art algorithms, such as random forests. Thus, in this paper, we implement different learning strategies and assess their performance on an individual basis, or in combination, regarding detection performance and memory and computational requirements. Results show that the best-performing algorithm, which is a combination of multi-centroid and multi-pass, can indeed reach the performance of the random forest model on a highly unbalanced dataset imitating a real-life epileptic seizure detection application.