Epi-Spec2State: Convolutional state space model via spectrogram image sequences for prediction-oriented seizure state classification in EEG signals.
- DOI
- 10.1016/j.cmpb.2026.109582
- Published
- 2026 Nov
- Container
- Computer methods and programs in biomedicine
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.cmpb.2026.109582,
title = {Epi-Spec2State: Convolutional state space model via spectrogram image sequences for prediction-oriented seizure state classification in EEG signals.},
author = {Zhang X and Chen Y and Ma Q and Liu Y},
year = {2026},
journal = {Computer methods and programs in biomedicine},
doi = {10.1016/j.cmpb.2026.109582},
url = {https://doi.org/10.1016/j.cmpb.2026.109582}
}RIS
TY - JOUR TI - Epi-Spec2State: Convolutional state space model via spectrogram image sequences for prediction-oriented seizure state classification in EEG signals. AU - Zhang X AU - Chen Y AU - Ma Q AU - Liu Y PY - 2026 JO - Computer methods and programs in biomedicine DO - 10.1016/j.cmpb.2026.109582 UR - https://doi.org/10.1016/j.cmpb.2026.109582 ER -
APA
X, Z., Y, C., Q, M., & Y, L. (2026). Epi-Spec2State: Convolutional state space model via spectrogram image sequences for prediction-oriented seizure state classification in EEG signals.. Computer methods and programs in biomedicine. https://doi.org/10.1016/j.cmpb.2026.109582
Source records
- pubmed · retrieved 2026-09-25T13:40:53.027Z