I ‘m working on the motor imagery data from BBCI2A we have 9 subjects and four kinds of imagery movement, in order to make a better classification with deep learning I decide to resource it to check what’s really going

for the classification problem , the bbci 2a dataset is a very good example to test any algorithm, consider the mne with python and tensorflow I try to deconding this dataset with MNE however  mne.find_event() has some  crutical error finding

this is a tutorial from MNe office webpage , I try to get though this and made some modifications import the moudle: import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import ShuffleSplit,cross_val_score from sklearn.pipeline import Pipeline from sklearn.discriminant_analysis import

LinearRegression  : simple linear regression output :array([ 3.03499549e-01, -2.37639315e+02, 5.10530605e+02, 3.27736980e+02, -8.14131709e+02, 4.92814588e+02, 1.02848452e+02, 1.84606489e+02, 7.43519617e+02, 7.60951722e+01])     Ridge regression : shrink the regression coefficients to zero output:     Lasso: least absolute shrinkage and selection operator, can set some coefficients

this two day spend lot of time on learning the MNE toturials it seems the guide is not well orginazied and should be polished several ideas are not good as I want keep on MNE I count on you