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This study investigates the efficacy of positive controls and detection floors in scalp-EEG speech decoding by auditing overt speech, articulator preparation, and imagined speech decoding across multiple datasets. The research reveals that overt speech decoding is heavily influenced by myogenic signals and that current decoding pipelines often fail to detect meaningful signals in relevant frequency bands, highlighting critical dataset pitfalls. The key result indicates that positive controls in common EEG speech decoding studies are unreliable, necessitating revised reporting standards that include band-limited accuracy, artifact-channel performance, and measured detection floors.
Common positive controls in EEG speech decoding are dominated by muscle artifacts, rendering them unreliable for validating imagined speech decoding pipelines.
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 the detection floor of five decoder families, and we checked the ds003626 labels against the dataset's acquisition protocol. Main results. On ds003626, two lip electrodes decoded overt speech at 0.7430 and the eight external channels at 0.7068, against 0.4231 for the 128-channel scalp montage, and no decodable signal survived in the 8-30 Hz band. On the other two corpora, within-subject accuracy rose monotonically with frequency into the range dominated by muscle activity. Band-power logistic regression detected an injected 8-30 Hz desynchronization of 0.54 uV under a criterion fixed in advance, whereas a compact convolutional network (EEGNet) detected nothing within subject up to 4.31 uV. In ds003626, the condition codes are the reverse of the intuitive reading, the class encoding changed between public releases, and condition is a deterministic function of time within each session. Significance. In these corpora the overt-speech positive control is dominated by myogenic signal and cannot validate a decoding pipeline. We recommend that speech-decoding studies report band-limited and artifact-channel accuracy, the direction of accuracy across frequency, and a measured detection floor.