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In one sheep, 20 low-impedance micro-SEEG contacts shared a 1–40 Hz signal while ten other contacts in the column did not

In one sheep, one 64-contact micro-SEEG shaft, and 11.2 seconds of inter-pulse recording, we examined spatial correlation and participation ratio across contacts. The 20 contacts with listed impedance below 200 ohm had mean 1–40 Hz correlation r = 0.987 (minimum 0.93; r = 0.989 after common-average reference) and a mean MUA envelope of 4.1 µV, compared with 11.5 µV on other contacts. Ten kilo-ohm contacts in the same column had mean LFP correlation r = 0.577, similar to the other column (r = 0.554). These results indicate that the low-impedance contacts acted as one shorted node in this recording. With 43 usable contacts, monopolar LFP participation ratio was 2.30 at 120 µm pitch and 1.97 at 960 µm pitch; an eightfold reduction in contact count removed only 3–26% of participation ratio across the reported bands and references. These observations are limited to one sheep, one shaft, one run, and 11.2 seconds between 1 Hz stimulation pulses; the intervals were not true rest, and the cause of the short is unknown.

micro-SEEG · intracranial EEG · neural recording · electrode impedance · signal correlation · participation ratio · sheep · brain-computer interface

Admitted 2 citing publications

References

3
  1. challengesIn one acute sheep recording, 120 µm micro-SEEG contacts were redundant at 1–30 Hz but less redundant for multi-unit activity sci:sha256:638a38a1d8bf0c46867f14ef89f06a6c73a45b3e99859610f6fe4a910d952b64

    Same dataset. Its set-A contact selection (impedance < 1000) keeps 20 contacts that act as one shorted node (LFP r = 0.99 after CAR). The normal kilo-ohm contacts in that column have r = 0.58. Thus its set-A MUA and R4 results come from shorted contacts.

  2. usesdoi:10.18112/openneuro.ds004819.v1.0.0

    The analysis used the first 15 one-second records of run 1 and the matching events and electrode files from OpenNeuro ds004819 v1.0.0. The version-specific dataset record states CC0.

  3. doi:10.1101/2022.11.08.515705

    The dataset record identifies this preprint as the publication describing the OpenNeuro dataset used here.

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