Epilepsy is a chronic neurological disorder with a significant prevalence. However, there is still no adequate technological support to enable epilepsy detection and continuous outpatient monitoring in everyday life. Hyperdimensional (HD) computing is an interesting alternative for wearable devices, characterized by a much simpler learning process and also lower memory requirements. In this work, we demonstrate a few additional aspects in which HD computing, and the way its models are built and stored, can be used for further understanding, comparing, and creating more advanced machine learning models for epilepsy detection. These possibilities are not feasible with other state-of-the-art models, such as random forests or neural networks. We compare inter-subject similarity of models per different classes (seizure and non-seizure), then study the process of creation of generalized models from personalized ones, and in the end, how to combine personalized and generalized models to create hybrid models. This results in improved epilepsy detection performance. We also tested knowledge transfer between models created on two different datasets. Finally, all those examples could be highly interesting not only from an engineering perspective to create better models for wearables, but also from a neurological perspective to better understand individual epilepsy patterns.

翻译：癫痫是一种患病率较高的慢性神经系统疾病。然而，目前仍缺乏足够的技术支持，以便在日常环境中实现癫痫检测和连续的门诊监测。超维计算作为一种可穿戴设备的理想替代方案，具有学习过程更简单、内存需求更低的显著特点。在本研究中，我们展示了超维计算及其模型构建和存储方式的几个额外优势，可用于进一步理解、比较和创建更先进的癫痫检测机器学习模型。这些可能性是其他先进模型（如随机森林或神经网络）无法实现的。我们比较了不同类别（癫痫发作与非发作）下受试者间模型的相似性，随后研究了从个性化模型构建通用模型的过程，并最终探讨了如何结合个性化模型与通用模型创建混合模型。这显著提升了癫痫检测性能。此外，我们还测试了两个不同数据集上模型之间的知识迁移能力。最后，这些示例不仅从工程角度为可穿戴设备构建更优模型具有重要价值，从神经科学角度而言，也有助于更深入地理解个体癫痫发作模式。