This study presents a novel approach to seizure detection by leveraging the unique capabilities of Dendritic LIF models. By applying these models to both EEG and ECoG(iEEG) datasets, we aim to improve the accuracy and reliability of seizure detection algorithms. Our research harnesses the heterogeneity inherent in Dendritic LIF models to better capture the dynamics of epileptic activity, paving the way for more effective diagnostic tools and treatments in epilepsy management.
Code can be updated according to desired and relevant datasets. Code inspired by the research of:
Zheng, Hanle, Zhong Zheng, Rui Hu, Bo Xiao, Yujie Wu, Fangwen Yu, Xue Liu, Guoqi Li, and Lei Deng. "Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics." Nature Communications 15, no. 1 (2024): 277.