Hyperdimensional Computing With Multiscale Local Binary Patterns for Scalp EEG-Based Epileptic Seizure Detection

Epilepsy is a common condition that causes frequent seizures, significantly impacting patients' daily lives. Non-invasive EEG is an effective tool for detecting seizure onset. Wearable EEG devices enable real-time monitoring and timely intervention but pose new algorithmic challenges on small model weight sizes and limited training data. Brain-inspired hyperdimensional computing (HDC) presents a potential solution for its small weight size and quick learning ability. Combining local binary pattern (LBP) codes with HDC can capture dynamic features in EEG time series. However, traditional LBP features may not offer sufficient robustness for trend modeling due to their high localization on individual samples, particularly on low-amplitude and non-stationary scalp EEG signals. To address the above challenges, this paper proposes a multi-scale LBP-based HDC (MSLBP-HDC) approach for scalp EEG analysis. Unlike traditional LBP-based HDC focusing only on the local change trend, the designed MSLBP-HDC extracts dynamic features at different time resolutions to detect abnormal cortical oscillations. The lengths of multiple temporal scales in MSLBP-HDC are determined based on the duration of spikes. Our results demonstrate that MSLBP-HDC has the highest specificity for all test seizure types and achieves competitive macroaveraging accuracy with the smallest model weight size in detection, compared to advanced deep learning, support vector machine, and HDC methods. Regarding few-shot learning performance, MSLBP-HDC outperforms existing approaches and achieves high accuracy using only 1% of the training data. Moreover, feature interpretability analysis from space and time domains highlights that MSLBP-HDC successfully extracts seizure-relevant features rather than noise or artifacts, ensuring the algorithm's reliability.