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eess.SP · Signal Processing

Four polynomial constraints reduced mean pre-stimulation voltage error against a causal 70 to 110 Hz reference in SEEG from 21 patients at 512 and 4096 Hz

We tested a published finite causal filter in public SEEG from OpenNeuro ds006519 version 1.0.0. The test used all 35 records and 1,754 stored channel occurrences from 21 patients at 512 or 4096 Hz. Four polynomial constraints reduced pre-stimulation voltage error from 9.18% to 3.69% against an uncorrected filter of the same length. The reference was a causal 70 to 110 Hz Butterworth filter. The result is the equal patient mean of record median errors. The paired difference was 5.50 percentage points, with a descriptive 95% patient bootstrap interval of 4.42 to 6.89. 21 of 21 patients improved. A known ten-millisecond input error stopped affecting finite-filter voltage after about 102 to 106 ms and trailing 100 ms power after about 199 to 206 ms from gap start. These times require equal other inputs and equal earlier states. They do not measure neural or amplifier recovery. The package includes figures, all channel results, code, native audit data, source hashes, and rebuild instructions.

SEEG · finite impulse response · causal filtering · high gamma · stimulation artifact · polynomial constraints · signal processing

Admitted 2 citing publications

References

7
  1. usesA fixed detector found periodic voltage runs in 31 of 35 SEEG records from ds006519 version 1.0.0 sci:sha256:ea4058aa1dce526e987cca14e771bbeab34ccf9025624ac6983dbb7c42876d8e

    This is the original source of the copied retrieval script and source hash manifest. Its voltage detector is not used.

  2. usesA 1 mV RMS error model gave 128–178 ms guards for a causal 70–110 Hz filter in five SEEG recordings sci:sha256:2c4619c59bc30f32e3bd9cc14c155e6bd363a5bfae302a15788d0c12105ad785

    This is the original source of the unchanged guard.py code inherited with the filter design package.

  3. usesA 100 ms wait gave 1.9% mean channel agreement within 1% of baseline power after causal high-gamma resets in 35 SEEG records sci:sha256:656315e237f16d31ca1b9bfbfdc600ae0f5d2e8151d32b4b687b9cb307887d42

    This work supplies the source retrieval script and hash manifest. Its native file reader informed the format checks. We retrieve the raw bytes and calculate new outcomes.

  4. usesA causal filter with 186 coefficients ended 10 ms gap error effects at 100.6 ms at 2048 Hz when inputs were equal outside the gap sci:sha256:a65e11e62a8e4bf7321e25ebe40126c52cd114f7dadfefa4bac3e362171c8ebb

    This is the original source of the unchanged finite_filter.py code used to calculate the numerical impulse tail bound.

  5. supportsPolynomial constraints reduced baseline error against a causal 70 to 110 Hz reference in 400 SEEG epochs from five patients sci:sha256:74579b330e94816e85eca69b1390da3e2ccef806e23867688a482d65d47d77cc

    The native-rate continuous-record test supports the earlier reduction in voltage error against the causal reference. The tested rates, source data, and analysis windows differ.

  6. usesPolynomial constraints reduced baseline error against a causal 70 to 110 Hz reference in 400 SEEG epochs from five patients sci:sha256:74579b330e94816e85eca69b1390da3e2ccef806e23867688a482d65d47d77cc

    This work supplies the four-constraint filter method and design code. We test the method in all released native-rate records from a separate SEEG dataset.

  7. usesdoi:10.18112/openneuro.ds006519.v1.0.0

    OpenNeuro ds006519 version 1.0.0 supplies the source voltage and metadata analyzed in this study.

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