This is implementation of Beta(4-7Hz), Theta(13-25Hz) extraction for EDF formatted EEG data by spectogram. and LDA classification of cognitive process for "Albasri, Ahmed (2019), EEG dataset of Fusion relaxation and concentration moods”, Mendeley Data, V1, doi: 10.17632/8c26dn6c7w.1
mne, scipy, matplotlib, numpy, sklearn, pandas required.
!pip install mne scipy matplotlib numpy sklearn pandas
fileToLabeld returns labeled theta/beta data for channel wise by given path(edf). signal p8 skipped due to dataset errors so shape of return is (256(hz)*180(sec), 13(channels)) for EEG concentration dataset.
folderToLabeled returns concatenated dataset from specified folder.
getData returns (train, test) data according to given params.
classify returns (input_data, labeled)
Edf dataset should located at ./edfs.
Hyungi Cho (Catholic Univ., @jasonhk24)