Hello!
I am fairly new to MNE and wanted to ask for advice on the best course of action for a project broadly looking into how different ERPs are elicited in response to fixations to different video ROIs (eyes, mouth, etc.) when watching 17 mins worth of videos. We are defining each fixation to an ROI as an event.
Our current pipeline does the following:
- Apply notch filtering (60Hz) and band-pass filtering (l_freq=0.1, h_freq=50.0)
- find and interpolate bad channels
- Run ICA (n_components=20) and keep “brain” and “other” components
- Segment the data into epochs and reject bad epochs
The data still looks a bit noisy, and the noise is highly variable depending on the participant.
- Do you think this preprocessing pipeline is appropriate for our dataset?
- Given that events (fixations) differ in terms of fixation type, fixation duration, and sequence of fixations per participant (depending on what each person generally finds interesting), it might also be possible that brain responses are overlapping (eg. response for fixations to the eyes are decreasing the response for the prior fixation to mouth because of how quickly it happened). Would you recommend a deconvolution method to disentangle overlapping responses to adjacent fixations after ICA in the preprocessing pipeline?
Thank you for your help!