Vinícius R. Carvalho*, Márcio F.D. Moraes, Antônio P. Braga, Eduardo M.A.M. Mendes Programa de Pós-Graduação em Engenharia Elétrica – Universidade Federal de Minas Gerais – Av. Antônio Carlos 6627, 31270-901, Belo Horizonte, MG, Brasil. Núcleo de Neurociências, Departamento de Fisiologia e Biofísica, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.

*[email protected]

Scripts to decompose EEG signals from the Bonn University [1] and NSC-ND [2] datasets, according to five methods: Empirical Mode Decomposition (EMD) and its extensions (EEMD, CEEMDAN), Empirical Wavelet Transform (EWT) and Variational Mode Decomposition.

Two datasets:

1. University of Bonn EEG seizure dataset
2. Neurology & Sleep Centre, Hauz Khas, New Delhi dataset
   Download EEG data to (1) BonnDataset/data/ and (2) NSC_ND/data/

"main_feats.py" opens and decomposes each EEG into N modes with EMD, EEMD, CEEMDAN, VMD and EWT. Features from each mode are then extracted and written in .csv files.

"main_classify.py" opens .csv files with features generated by "main_feats.py". Samples are split into training/testing sets into 5-folds for cross-validation. Several classifiers are evaluated and mean performance results are presented for 10 iterations.

"main_figs.py" plots figures that appear on the manuscript The following packages are required: numpy, scipy, scikit-learn, pandas, matplotlib, pyEMD, ewtpy, vmdpy, seaborn.

Citation: Vinícius R. Carvalho, Márcio F.D. Moraes, Antônio P. Braga, Eduardo M.A.M. Mendes, Evaluating five different adaptive decomposition methods for EEG signal seizure detection and classification, Biomedical Signal Processing and Control, Volume 62, 2020, 102073, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2020.102073.