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Qiyou Wu
Artificial Intelligence, Northeastern University, MA, USA
Computer Science & IT

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Calibration-Light Subject-Independent Motor Imagery BCI via Self-Supervised Pretraining and Conformer Qiyou Wu; Gaotian Mi; Dan Wood
Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.493

Abstract

Motor imagery (MI) electroencephalography (EEG) is a foundational paradigm for non-invasive brain–computer interfaces (BCIs). However, its practical adoption is constrained by time-consuming per-user calibration and limited cross-subject generalization. This study evaluates a calibration-light MI-BCI framework that combines self-supervised masked EEG pretraining with a lightweight Conformer fine-tuning model. Experiments were conducted on BCI Competition IV Dataset 2b using only the labeled sessions 01T–03T, with artifact-annotated trials removed according to the official 1023 markers. Three deployment-relevant settings were examined: within-subject evaluation (01T–02T → 03T), strict leave-one-subject-out (LOSO) evaluation, and few-shot adaptation with k = 1/5/10 trials per class from the held-out subject’s screening sessions. Full within-subject benchmarking included CSP+LDA, EEGNet, DeepConvNet, ShallowFBCSPNet, supervised Conformer, and SSL+Conformer, while the subject-independent and few-shot analyses focused on CSP+LDA, EEGNet, supervised Conformer, and SSL+Conformer. In the fully calibrated setting, the best mean accuracy was obtained by ShallowFBCSPNet (62.23% ± 14.16%), whereas SSL+Conformer achieved 54.85% ± 11.15% and slightly outperformed the supervised Conformer (53.56% ± 8.81%). Under strict LOSO, EEGNet achieved the highest mean accuracy (52.92% ± 8.25%), while SSL+Conformer reached 51.56% ± 7.18%. In few-shot adaptation, SSL+Conformer achieved the highest mean accuracy at k = 10 (52.84% ± 7.64%) among the core calibration-light methods. The proposed model had a size of 0.1329 MB, a median CPU latency of 0.8777 ms/trial, and LOSO calibration values of ECE = 0.0630 and Brier = 0.4995. These results indicate that masked EEG pretraining provides a competitive lightweight baseline and is most useful when a modest amount of target-subject calibration data is available.
Privacy-Robust Incrementality Estimation in Cookieless Settings via Uplift Modeling: Reproducible Evidence from the Hillstrom E-Mail Experiment Jingwen Bai; Haozhe Wang; Qiyou Wu; Boning Zhang
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.468

Abstract

Measuring advertising incrementality in the absence of user-level identifiers is increasingly constrained by platform policies and privacy regulations. In cookieless environments, practitioners often observe only aggregated or weak signals (e.g., cohort-level conversion counts) and must still estimate the causal lift of an intervention while quantifying uncertainty. This paper studies cookieless incrementality evaluation through the lens of uplift and individual treatment effect (ITE) modeling under explicit privacy constraints. We conduct full experimental evaluations on the MineThatData (Hillstrom) E-Mail Analytics Challenge dataset (64,000 customers in a randomized controlled experiment with three arms). We cast the task as a binary treatment problem—sending any e-mail campaign versus sending none—and compare six ITE estimators (S-, T-, X-, R-, and doubly robust learners, plus transformed-outcome regression) against cohort-only estimators that emulate cookieless measurement. The cohort estimator uses only aggregated counts and a Bayesian beta–binomial model to shrink noisy rates, and we evaluate robustness under k-anonymity thresholds and Laplace-noised differentially private aggregates. Across held-out test data, the best ID-level model (T-learner with logistic regression) achieves a Qini coefficient of 6.675 and improves the estimated policy conversion rate when targeting the top 20% of customers by predicted uplift. Cohort-only estimation retains a weaker and more variable signal; its point estimate is sensitive to privacy constraints but yields valid uncertainty intervals with 0.892 empirical coverage for a 95% interval in cohort-level validation. The results demonstrate that (i) causal lift is estimable without identifiers when randomized experimentation is available, (ii) doubly robust estimators provide strong performance and fast scoring, and (iii) privacy-preserving aggregation introduces an accuracy–privacy trade-off that can be quantified and monitored using bootstrap and Bayesian uncertainty.
Co-Authors Boning Zhang Dan Wood Gaotian Mi Haozhe Wang Jingwen Bai