Data analyses¶
for details please download the script
general information¶
main analysis:
mne python
other packages used:
os
numpy as np
pandas
matplotlib.pyplot
csv
pickle
Overview¶
1. reading in the data & filtering¶
montage: standard 10 -20 system
64 electrodes + misc -> misc channels excluded
subject names : 001 - 013
blocks 01 - 03
exclude Fp1 -> bad channel - Fp1 seemed to be locaded at a wrong position (somewhere posterior)
filtering data with: low freq: 1Hz, high freq: 50Hz
eeg reference set to “average”
filtered data was saved as “s{subnum}_b{block}-raw.fif”
2. ICA¶
fit ica on a copy of the data
ica ran with 15 components
ICA saved as: “s{subnum}_b{block}-ica.fif”
take blink templates from participant 01 block 01 (vertical and horizontal eye movements)
automatic detection and labeling of blink components using corrmap -> threshold 0.85
appyling the ica
data with applied ICA saved as “s{subnum}_b{block}-raw.fif” in a different folder than the filtered raw data
3. averaging¶
making epochs based on events
events taken into account:
standard
target
non target
other events
response
no response
conditions:
standard
target
non target
firt repetition after deviant
last repetition after deviant
averages made for each participant in each condition and saved into dictionaries
averages were turned into lists and saved using pickle
grand average calculated and plotted using mne.grand_average