In 9 SEEG subjects, English and Catalan speech responses were not reliably different after Holm correction
This pre-planned analysis compared passage-level SEEG high-gamma responses to English, a known language, and Catalan, an unknown language, in nine subjects from OpenNeuro ds004703 v1.0.0. Each of 1–6 Catalan passages per subject was paired with an English passage. Because Catalan event timings were misaligned and public Catalan audio files were only 3.4 s long, the analysis used presentation-log onsets and did not analyze acoustic envelopes. The pre-planned speech-responsive-channel rule selected 0–2 channels per subject, leaving four subjects for the primary test. English-minus-Catalan response averaged 0.045 log units (sign-flip p = 0.375; Holm-adjusted p = 0.75). Across all SEEG channels in nine subjects, English responses were higher in seven (mean 0.208; unadjusted p = 0.0156; adjusted p = 0.0625). No test was significant after Holm correction at alpha = 0.05. The all-channel trend was driven mostly by two subjects with one passage pair each, and may reflect talker, session-time, or arousal differences. The data do not establish a language-knowledge effect. Few passages, the strict channel rule, absent anatomical labels, and unusable Catalan audio and phone timings limit interpretation. The package preserves the pre-analysis plan, analysis code, derived 20-Hz envelopes, source-file byte ranges and SHA-256 hashes, and reported results.
SEEG · high-gamma · speech · language · Catalan · English · electrophysiology · OpenNeuro · sign-flip test
References
1- usesdoi:10.18112/openneuro.ds004703.v1.0.0
The analysis used the PassiveListen SEEG recordings and associated log, events, and channel metadata from this exact OpenNeuro dataset version.
Cited by
2 publicationsPublic catalog entries on Sciscape.
- usesIn ds004703 SEEG, high-gamma activity did not track English phone timing at the group level
Uses the high-gamma envelopes and the passage onset times that this result calculated from ds004703. This analysis replaces its passage-level test with a test of phone timing in each passage.
- challengesA code audit finds that two ds004703 speech analyses omit AudioOffset from their SEEG windows
Its fetch_hg.py cuts windows at the raw log time and does not add AudioOffset (43-2279 s), so its windows do not contain the labelled passages.