A Capacity-Matched Test of Observed EEG Amplitude for Motor-Imagery Error Ranking

Selective prediction lets a motor imagery system defer uncertain electroencephalography (EEG) trials. We ask whether an observed-amplitude score helps rank decoder errors beyond class probabilities and available channels. We test this on a task built from participants imagining movements of the left or right hand in the PhysioNet EEG Motor Movement/Imagery Database. We measure the error rate among accepted trials with normalized partial area under the risk-coverage curve (pAURC), from accepting half of all trials to accepting every trial. Lower values mean fewer accepted-trial errors. In a follow-up comparison, a permutation sham had the same number of model coefficients as the amplitude selector. Under synthetic electromyographic (EMG) contamination, the full selector minus sham pAURC difference was 0.021% lower than under clean trials. Its participant-level interval included zero. Refitting decoders on separate source participants also left the result unresolved. The earlier comparison with a smaller selector did not meet its prespecified criterion and changed model size, so it cannot isolate the amplitude score. The evidence does not resolve whether the score improves error ranking.