False-Positive Calls of Reimplemented Published Analyses for Covert Command-Following on Electroencephalograms Recorded Without Study Commands
Authors: A. Gorenshtein, Y. Adiniaev, Y. Barash, E. Klang, O. Daniel
Abstract
ObjectiveTo measure how often published electroencephalographic analyses for covert command-following call patients positive without commands, and their detection of genuine responses. MethodsThis 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. ResultsThree 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 2011s 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. InterpretationReimplemented published analyses called up to 27% of patients positive without commands, more with repeated runs. A positive result needs its complete call rules false-call rate and detection at that rate; imagery-versus-rest detection may reflect task engagement, not imagery.
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This audit tests imagined-speech EEG decoding with injected effects and positive controls, addressing detection limits and validation pitfalls rather than command-free false calls.
Abstract
Objective. Studies that decode imagined speech from scalp electroencephalography (EEG) commonly validate their pipelines with a positive control, usually overt speech, and report null results without stating what effect size the pipeline could have detected. Approach. Using ds003626 ("Thinking out loud"), KaraOne and Track 3 of the 2020 International Brain-Computer Interface Competition, we audited decoding of overt speech, articulator preparation and imagined speech with artifact-channel subsets, band-limited decoding and trial selection, under a protocol that enforces split integrity by assertion. We injected class-dependent effects of known amplitude into the real recordings to measure th…
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Beyond Classification Accuracy: Building Trustworthy Artificial Intelligence for Clinical Electroencephalography.Sha-Sha Hu, Yun-Lei Huang, Jie Zhang · 2026 · Brain Research Bulletin · Not summarized yet
This article discusses why high EEG classification accuracy does not establish clinical readiness, while the paper measures false calls of command-following analyses.
Abstract
Electroencephalography (EEG) remains important in clinical neuroscience because it records brain activity as a time-resolved physiological process. It offers millisecond temporal resolution, can be used at the bedside and supports prolonged monitoring. Artificial intelligence (AI) has expanded EEG analysis through automated pattern recognition, representation learning, large-scale screening and decision support in epilepsy, sleep medicine, cognitive disorders, psychiatric conditions, intensive care, disorders of consciousness and anaesthesia monitoring. High classification accuracy in a research dataset, however, is not clinical readiness. EEG signals are shaped by physiological state, artef…
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This study decodes auditory expectations in prolonged disorders of consciousness, a patient population also included in the paper's command-free false-call tests.
Abstract
OBJECTIVE Electroencephalogram (EEG) features are important objective markers for diagnostic and prognostic evaluation of prolonged disorders of consciousness (DOC). However, task-based EEG paradigms are limited by mismatches between task demands and patients' residual awareness, as well as the limited individual assessment capacity of machine-learning-based detection frameworks. APPROACH In this study, we proposed a lowdemand DOC detection strategy by decoding expectations of global regularities in sequence auditory stimulation using a modified local-global paradigm. Three single-trial classification frameworks using canonical correlation analysis, principal component analysis, and xDAWN fo…
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This study analyzes EEG microstates after cardiac arrest and relates them to neurological outcome, whereas the paper tests command-following calls in post-arrest recordings.
Abstract
Electroencephalography (EEG) is central to neurological assessment after cardiac arrest, yet conventional approaches predominantly characterize amplitude, frequency content, continuity, suppression, reactivity, or predefined pathological patterns. EEG microstates provide a complementary framework by describing quasi-stable configurations of the global scalp electric field. We investigated whether an outcome-blind microstate decomposition of EEG from comatose survivors of cardiac arrest identifies reproducible spatial states associated with neurological outcome. We performed a secondary analysis of the multicenter I-CARE/PhysioNet database. Following prespecified EEG screening requiring conti…
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Same pipelines, different answers: how evaluation design shapes EEG seizure predictionz. song, M. Pedersen, D. Henare · 2026 · medRxiv preprint · AI summary available
This study holds seizure-prediction pipelines fixed while changing evaluation splits, examining how evaluation design affects EEG results rather than false command-following calls.
In short
Fixed EEG seizure-prediction pipelines performed near the 0.5 AUC reference on unseen patients, while window allocation and threshold transfer substantially changed their apparent performance.
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Variability in Preprocessing and Analysis of the Error‐Related Negativity in Adult Response‐Conflict Studies: A Systematic ReviewKatharina Paul, Johanna Paping, Jan Wacker · 2026 · Psychophysiology · Not summarized yet
This systematic review catalogs preprocessing and analysis choices in EEG error-related negativity studies, rather than testing command-following analyses on command-free recordings.
Abstract
ABSTRACT The analysis of EEG (electroencephalography) data affords many defensible choices, creating substantial researcher degrees of freedom. While stimulus‐locked components such as the N400 show wide methodological variability and incomplete reporting, it has been unclear whether this generalizes to the response‐locked error‐related negativity (ERN). We conducted a PRISMA‐guided systematic review of ERN studies published 1995–2025. From a chronologically stratified sample of 202 studies of adults using conventional response‐conflict tasks and conventional ERP analyses, we extracted preprocessing and quantification choices, assessed reporting completeness, examined temporal trends, and te…
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This study uses stimulation-evoked EEG measures to assess consciousness in prolonged disorders of consciousness, a neighboring clinical application to covert command-following.
Abstract
Objective. Consciousness diagnosis in prolonged disorders of consciousness (pDoC) relies on the Coma Recovery Scale–Revised, yet consciousness or its potential for recovery may escape behavioural detection. We tested whether near-nerve stimulation (NNS) of the median nerve, compared to standard surface cutaneous stimulation (SCS), could provide an EEG-based marker of consciousness and its recovery, by specifically eliciting sensory responses. Approach. Nineteen pDoC patients (median age 69 years, time post-onset 74 d) underwent NNS and SCS with high-density EEG. We quantified evoked potentials, global field power and perturbational complexity, and defined recovery as improvement in clinical …
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Measuring Evaluation Leakage in EEG Motor Imagery Decoding, with an Application to Stroke PatientsAria Chadha · 2026 · Proceedings of the OJSS Virtual Research Conference 2026 · Not summarized yet
This study quantifies evaluation leakage in EEG motor-imagery decoding, while the paper examines false-positive calls and detection in covert command-following analyses.
Abstract
Brain-computer interfaces (BCIs) that decode imagined movement from EEG are often evaluated using Common Spatial Patterns (CSP), a supervised filtering method. If CSP is fitted to all data before splitting into training and test sets, information from the test labels leaks into the model and inflates reported accuracy. This study measures the size of that effect. In a null experiment where true accuracy should be at chance (50%), fitting CSP before the split produced 91% within-subject accuracy, a 41-percentage-point inflation, compared with about 5 points in cross-subject evaluation. On real data from 87 healthy participants and 46 post-stroke patients, leakage added 14.3 and 10.6 points re…
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