# Shift-invariant waveform learning on epileptic ECoG

arXiv: https://arxiv.org/abs/2108.03177 · HTML: https://mlsanity.com/paper/2108.03177/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 6 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding](https://mlsanity.com/paper/1705.08006/) (2017)
- [Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals](https://mlsanity.com/paper/1805.09654/) (2018)
- [Searching for waveforms on spatially-filtered epileptic ECoG](https://mlsanity.com/paper/2103.13853/) (2021)

## Built on by (most cited first, 3 of 3)

- [Convolutional Monge Mapping between EEG Datasets to Support Independent Component Labeling](https://mlsanity.com/paper/2509.01721/) (2025)
- [Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes](https://mlsanity.com/paper/2607.22508/) (2026)
- [QSMP: finding representative time series subsequences through Quick Shift+Matrix Profile](https://mlsanity.com/paper/2608.15492/) (2026)

## Abstract (arXiv)

Seizure detection algorithms must discriminate abnormal neuronal activity associated with a seizure from normal neural activity in a variety of conditions. Our approach is to seek spatiotemporal waveforms with distinct morphology in electrocorticographic (ECoG) recordings of epileptic patients that are indicative of a subsequent seizure (preictal) versus non-seizure segments (interictal). To find these waveforms we apply a shift-invariant k-means algorithm to segments of spatially filtered signals to learn codebooks of prototypical waveforms. The frequency of the cluster labels from the codebooks is then used to train a binary classifier that predicts the class (preictal or interictal) of a test ECoG segment. We use the Matthews correlation coefficient to evaluate the performance of the classifier and the quality of the codebooks. We found that our method finds recurrent non-sinusoidal waveforms that could be used to build interpretable features for seizure prediction and that are also physiologically meaningful.