AI Insight
This study tested 11 published EEG analysis methods designed to detect covert command-following in patients by applying them to recordings where no commands were given. Three methods incorrectly identified 13.8% to 23.3% of patients as following commands when none were present, well above the expected 5% false-positive rate, with rates reaching up to 27.3% in cardiac arrest patients. The researchers identified specific technical issues causing these false positives, including inappropriate statistical variance calculations, selection bias across multiple testing configurations, and overly permissive thresholds.
Why it matters
These findings raise serious concerns about the reliability of EEG-based assessments used to detect consciousness in severely brain-injured patients. Incorrectly identifying unresponsive patients as consciously following commands could lead to misdiagnosis and inappropriate treatment decisions in critical care settings.
⚠️ Preprint – Noch nicht peer-reviewed
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Objective: To measure how often published electroencephalographic analyses for covert command-following call patients positive without commands, and their detection of genuine responses. Methods: This cross-sectional study applied 11 reimplemented published analyses to electroencephalograms without commands: 974 clinical electroencephalography patients and, with rules fixed, 730 new patients; 38 with prolonged disorders of consciousness; and 544 comatose patients after cardiac arrest. Outcomes: false calls (share called positive) against a 5% benchmark, and genuine-response detection. Results: Three of 10 command-following analyses called significantly more than 5% of clinical electroencephalography patients positive, replicated in new patients: Goldfine 2011, a spectral test, 23.3% (95% CI, 20.2%-26.6%), above its stated 5%; du Bois 2026, a criterion for entry to brain-computer interface training, 22.2% (95% CI, 19.2%-25.5%); and Guger 2018, a P300 accuracy threshold, 13.8% (95% CI, 11.5%-16.3%), near its approximate 12% chance rate (24.5% over 2 runs). The other 7 called 0.0% to 6.9%. After cardiac arrest: 11.5% to 27.3%. All 3 detected genuine responses. Identified contributors: a variance ignoring within-command dependence (Goldfine 2011), selection over configurations and times (du Bois 2026) and a permissive threshold (Guger 2018). Goldfine 2011’s command-level correction, fixed before testing, called 0.8% of new patients and 2.4% after arrest, detecting 93.4% of stroke imagery-versus-rest sessions but few between imagery types. Interpretation: Reimplemented published analyses called up to 27% of patients positive without commands, more with repeated runs. A positive result needs its complete call rule’s false-call rate and detection at that rate; imagery-versus-rest detection may reflect task engagement, not imagery. Funding: A.G. and E.K. were supported by the Clinical and Translational Science Awards (CTSA) Grant UL1TR002541 from the National Center for Advancing Translational Sciences, through the Harvard Catalyst – The Harvard Clinical and Translational Science Center Pilot Award Program. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.