We propose EEG-VID, a task-guided latent predictive pretraining framework for EEG decoding under session and subject shifts. EEG-VID predicts future latent EEG states from recent history using an exponential-moving-average target encoder and weak task guidance, followed by supervised fine-tuning. Across VIG-48 and BCI Competition IV-2a/IV-2b, Stage 1 improves mean accuracy in 41 of 42 matched backbone-dataset-protocol comparisons, including all 12 leave-one-subject-out settings, with a maximum gain of 16.22 percentage points. On the 48-region cross-day VIG-48 task, EEG-VID achieves 6.52% Top-1 and 30.50% Top-5 accuracy. In a separate six-participant offline robot-scene study, candidate-constrained target selection reaches 40.24% versus a 25% chance level after subject-specific calibration. These results support task-guided latent prediction as a transferable pretraining strategy for EEG decoding and scene-constrained assistive target selection.
Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology, but EEG-PRIME achieves balanced accuracy comparable to within-session calibration models without target-domain optimization, calibration, or lin...
Shuai-Lei Zhang, Muyun Jiang, Wei Zhang et al.· 0 citations
Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to new users under limited target-user data and constrained compute. However, held-out-subject performance can be overstated when test-subject information influences preproce...
Abdul Basit, S. Rehman, Muhammad Shafique· 0 citations
EEG-Fusion is presented, a failure-informed decision-level fusion framework that treats source-free MI decoding as label-free reliability estimation over heterogeneous experts and suggests that label-free reliability estimation can reduce subject-level failure modes in source-free MI-EEG deployment.
Abdul Basit, S. Rehman, Muhammad Shafique· 0 citations
A novel Prototype-driven online EEG Decoding framework (PRED) that incorporates a Subject Shift Detector based on policy stability to reliably identify latent domain shifts unsupervisedly, thereby constructing adaptive soft boundaries and a Prototype-guided Shift Correction mechanism that leverages a multi-granular mem...
Shaoqi Zhang, Xi Jin, Xiaojun Ning et al.· Proceedings of the Thirty-Fi...· 0 citations
EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especially across diverse clinical datasets. Full fine-tuning is impractical for resource-constrained clinical settings due to high computational requirements. In this work, we i...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
MIT News · Artificial Intelligence· news.mit.eduSep 29, 2026
Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.
