| # Average referencing gives inconsistent results across application routes for mixed-modality recordings |
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| I am preprocessing mixed-modality intracranial recordings with the |
| supplied MNE-Python source snapshot. My recordings contain channels of |
| several types (for example sEEG depth contacts and ECoG grid contacts in |
| the same file), and I re-reference them with an average reference over |
| the channel types I care about, using `set_eeg_reference`. |
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| The library offers two routes for this — direct application and the |
| projection route — and both are required to implement the same |
| operation. For valid inputs both routes complete without errors, but the |
| referenced results are not consistent with the average-reference |
| expected average-reference semantics, and the two routes do not agree with |
| each other on mixed-type recordings. On single-modality recordings |
| everything behaves as documented. |
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| Run `python reproduce.py` from the task directory. It builds the |
| synthetic recordings in `fixtures/recording_manifest.json`, applies the |
| average reference through both routes, and reports the post-referencing |
| subset means and the deviation between the routes. |
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| Inspect the source snapshot and the public reproduction, and repair the implementation so that average referencing |
| behaves according to the documented semantics for all valid inputs: any |
| combination of supported channel types, any channel counts, and both |
| application routes. The repair |
| must not change behaviour that already conforms to the expected semantics |
| (single-modality referencing, explicit channel-list references, excluded |
| channel types), and must not hard-code the public fixtures, channel |
| names, or channel-type combinations. |
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| Keep the changes limited to the scientific implementation. Do not use the |
| network or add external data files. |
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