real-time EEG processing (Brainproducts)


I have my EEG data recorded through brainproducts system and streamed to client PC for further processing. I have a python-based RDA that is receiving the data real-time. The data is received in chunks (64 channels, 20 datapoints).

My question is, how can I use mne-real time for further processing (filtering, baseline correction, artifact rejection, band power calculation… etc)?

I am unable to find a starting point or a resource to utilize. Will appreciate your help!

Not directly answering your question, but perhaps you could look into LSL and GitHub - mne-tools/mne-lsl: A framework for real-time brain signal streaming with MNE-Python. as well.

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Thanks Stefan!

I actually realized I can better formulate my question…
Since I have my python client receiving data already, I was wondering if I can use the regular mne functions generally used for offline analysis after dumping my data in an mne-structure as described here: Creating MNE-Python data structures from scratch — MNE 1.7.0.dev98+ge6b49ea77 documentation

yes, that should be possible — once you have an MNE object (Raw, Epochs, etc) it should all “just work”. Did you try it and encounter a failure? If so please post the code and traceback.