Calibration-Light Cross-Subject Signal-Readiness Screening for Motor-Imagery BCI with Self-Supervised Conformers, Selective Prediction, and Evidence-Grounded LLM Feedback
Keywords:
Electroencephalography, Openbci, Signal Quality, Cross-Subject Transfer, Conformer, Self-Supervised Learning, Selective Prediction, Calibration, Nonmedical FeedbackAbstract
Low-cost electroencephalography can widen access to brain-computer interface research, but cross-subject deployment is limited by device heterogeneity, electrode saturation, and scarce calibration data. We examined the six continuous recordings in the OpenBCI multidirectional mouse-cursor collection as a signal-readiness benchmark. The repository describes four motor-imagery activities, eye blinking, and rest, yet the files contain no event boundaries and all marker values are zero. Accordingly, the study does not assign motor-imagery classes or report task-decoding accuracy. All 450,651 rows were audited, harmonized to eight channels at 125 Hz, and divided into 506 five-second windows. A deterministic target marked the upper within-subject artifact-burden quartile. Subject-held-out evaluation compared bandpower-LDA, descriptor-map CNN, descriptor Conformer, masked-descriptor self-supervision, covariance alignment, and eight-window target calibration. Unlabeled-target CORAL-logistic achieved 0.823 mean balanced accuracy. The supervised Conformer obtained 0.689, whereas masked pretraining decreased it to 0.579. At 80.44% realized coverage, confidence-based withholding reduced accepted-window error from 0.135 to 0.093. A persistent-rail hard gate prevented lower-relative-burden predictions from certifying a defective recording. The structured output layer rendered 18 rule-verified, nonmedical feedback cards from measured quality and confidence fields. The study establishes what these files support directly and identifies the event metadata required for valid activity decoding.
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