M
Mei-Lin Luo
Researcher at Beihang University
Publications - 6
Citations - 498
Mei-Lin Luo is an academic researcher from Beihang University. The author has contributed to research in topics: Time–frequency analysis & Epileptic seizure. The author has an hindex of 5, co-authored 6 publications receiving 364 citations.
Papers
More filters
Journal ArticleDOI
Automatic Epileptic Seizure Detection in EEG Signals Using Multi-Domain Feature Extraction and Nonlinear Analysis
TL;DR: Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures.
Journal ArticleDOI
Epileptic Seizure Classification of EEGs Using Time–Frequency Analysis Based Multiscale Radial Basis Functions
TL;DR: The experimental results indicate that the proposed MRBF-MPSO-SVM classification method outperforms competing techniques in terms of classification accuracy, and shows the effectiveness of the proposed method for classification of seizure epochs and seizure-free epochs.
Journal ArticleDOI
Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals Using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features.
TL;DR: Experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.
Journal ArticleDOI
A multiwavelet-based time-varying model identification approach for time-frequency analysis of EEG signals
Yang Li,Mei-Lin Luo,Ke Li +2 more
TL;DR: Simulation studies and applications to real EEG data elucidate that the proposed wavelet approach is capable of achieving a high time-frequency representation for nonstationary processes.
Journal ArticleDOI
High-resolution time-frequency representation of EEG data using multi-scale wavelets
TL;DR: In the new parametric modelling framework, the time-dependent parameters of the TVAR model are locally represented by using a novel multi-scale wavelet decomposition scheme, which can allow the capability to capture the smooth trends as well as track the abrupt changes of time-varying parameters simultaneously.