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Creating epochs
- ZAHRAN Saeed (Unlicensed)
After working with continuous data, we typically have to deal with evoked designs and and there we have to create epochs, meaning we have to slice the continuous data into quite a bunch of small time segments typically just a second or two seconds or so long and we analyze those. So in this App we're going to create epochs from continuous raw data.
First it reads the raw data, then extract the events, the user can add the description of the events, the user need to specify where does the epoch start/end relative to an event onset where does the epoch and also should we apply some form of baseline correction. To actually create these epochs then we use the mne function called mne.Epochs object, so we're going to pass the raw data we intend to cut into epochs, we have to tell it which events (events numpy array) shall be used to actually cut these epochs, we're also going to pass in the events dictionary, this will be used to assign human readable names, the time relative to each event at which to start and end each epoch and the baseline correction.
Links to the App:
Inputs of the App:
Files | Format | Datatype | Description | Optional |
|---|---|---|---|---|
Raw data | .fif | Data to process | No | |
Events | .tsv | Channels | Yes |
Configuation parameter | Type | Description |
|---|---|---|
tmin | number | The time relative to each event at which to start epoch (in seconds) |
tmax | number | The time relative to each event at which to end each epoch (in seconds) |
event_id_condition | string | Mapping the integer Event ID to experiment descriptions. The description will be used to label each event with more descriptive text rather than the original event ID |
Outputs of the App:
File | Format | Datatype | Description |
|---|---|---|---|
Epochs | .fif | neuro/meeg/mne/epochs | Epoched data |
Evoked | .fif | neuro/meeg/mne/evoked | Evoked data |
Report | .html | html | Visualization of raw, event and epoched data |