EEG-VID: Task-Guided Latent Predictive Pretraining for EEG Decoding and Assistive Target Selection

2026-08-31

AI Summary

该研究提出EEG-VID框架，通过预测未来脑电状态解决跨会话和个体差异的脑电解码问题，采用弱任务引导和监督微调方法，在多个数据集上提高了准确性。

Abstract

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.

Problem

• EEG Decoding
• Session and Subject Shifts
• Assistive Target Selection

Innovation

• task-guided latent predictive pretraining
• EEG decoding
• exponential-moving-average target encoder
• supervised fine-tuning
• assistive target selection