Using ICA for EEG noise filtering

Data preprocessing

Assume a 6-channel EEG (4-channel eeg+2 channel EOG) that generates Ndarray or matrix S,row for time Point,col as a channel. To standardize S:

S/= s.std (axis=0)

ICA analysis

Module Import:

 from Import Fastica

To build an ICA object, select fractions:

ICA = Fastica (n_components=6)

(The composition is randomly arranged, using random_state for repeatability analysis if necessary)

s corresponds to the composition of S_:

S_ = Ica.fit_transform (S)

The corresponding mix matrix is:

Ica.mixing_

Filter noise

For s_ mapping, X is time point,y for each component. Determine the source of the suspected noise.

For example, get:

Suppose 5th, 60% is divided into sources of noise, delete the ingredient, rebuild the fix s:corr_s

corr_s = Np.dot (s_[:,: 4], Ica.mixing_. T[:4,:])

Results

Filter before noise

After filter noise

Complete

Using ICA for EEG noise filtering