# Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection

arXiv: https://arxiv.org/abs/2205.07654 · HTML: https://mlsanity.com/paper/2205.07654/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 16 May 2022.

## Builds on (most cited first, 6 of 6)

- [In-memory hyperdimensional computing](https://mlsanity.com/paper/1906.01548/) (2019)
- [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)
- [Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection](https://mlsanity.com/paper/2111.08463/) (2021)
- [Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure Detection](https://mlsanity.com/paper/2201.09759/) (2022)
- [Analysis of Contraction Effort Level in EMG-Based Gesture Recognition Using Hyperdimensional Computing](https://mlsanity.com/paper/1901.00234/) (2019)

## 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)
- [Importance of methodological choices in data manipulation for validating epileptic seizure detection models](https://mlsanity.com/paper/2302.10672/) (2023)
- [Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data](https://mlsanity.com/paper/2402.17572/) (2024)
- [D2H-AD: A Hybrid Model Utilizing Hyperdimensional Computing for Advanced Anomaly Detection](https://mlsanity.com/paper/2606.13754/) (2026)

## Abstract (arXiv)

The healthcare landscape is moving from the reactive interventions focused on symptoms treatment to a more proactive prevention, from one-size-fits-all to personalized medicine, and from centralized to distributed paradigms. Wearable IoT devices and novel algorithms for continuous monitoring are essential components of this transition. Hyperdimensional (HD) computing is an emerging ML paradigm inspired by neuroscience research with various aspects interesting for IoT devices and biomedical applications. Here we explore the not yet addressed topic of optimal encoding of spatio-temporal data, such as electroencephalogram (EEG) signals, and all information it entails to the HD vectors. Further, we demonstrate how the HD computing framework can be used to perform feature selection by choosing an adequate encoding. To the best of our knowledge, this is the first approach to performing feature selection using HD computing in the literature. As a result, we believe it can support the ML community to further foster the research in multiple directions related to feature and channel selection, as well as model interpretability.