Epileptic seizure detection in EEG signals using sparse multiscale radial basis function networks and the Fisher vector approach

作者：

Highlights：

• A novel multiscale radial basis function networks for EEG seizure detection.

• High-resolution time-frequency images attained.

• Discriminative texture features based on the Fisher vector encoding.

• Two widely used independent datasets employed to test robustness.

• High classification performance obtained.

摘要

•A novel multiscale radial basis function networks for EEG seizure detection.•High-resolution time-frequency images attained.•Discriminative texture features based on the Fisher vector encoding.•Two widely used independent datasets employed to test robustness.•High classification performance obtained.

论文关键词：Electroencephalography (EEG),Fisher vector,Multiscale radial basis functions (MRBF),Modified particle swarm optimization (MPSO),Orthogonal least squares (OLS),Seizure detection

论文评审过程：Received 30 July 2018, Revised 14 September 2018, Accepted 17 October 2018, Available online 29 October 2018, Version of Record 19 December 2018.

论文官网地址：https://doi.org/10.1016/j.knosys.2018.10.029