Now we can ask this App to drop some of our epochs based on the amplitude of individual channels, by specifying a rejection criteria so we could for example say that if we have an eeg channel in our epoch that shows a an amplitude of 150 micro volts or above we're going to reject that epoch, There is also a related parameter, flat criteria, that can be used to set minimum acceptable peak-to-peak amplitudes for each channel type in an epoch. To actually apply these criteria we use the drop_bad function and we pass reject criteria and the flat criteria.
Inputs of the App:
Files | Format | Datatype | Description |
|---|---|---|---|
Epochs | -epo.fif | neuro/meeg/mne/epochs | Epoched data to be filtered |
Configuration parameter | Type | Discription |
|---|---|---|
Rejection criteria | number | Maximum acceptable peak-to-peak amplitudes for each channel type in an epoch |
Flat criteria | number | Mininmum acceptable peak-to-peak amplitudes for each channel type in an epoch |
Outputs of the App:
Files | Format | Datatype | Description |
|---|---|---|---|
Epochs | -epo.fif | neuro/meeg/mne/epochs | Epoched data after the artifact rejection |
Figure | .png | png | A summary of the causes of rejected epochs |