ThinkNet：面向受试者无关MI-EEG解码的紧凑架构选择与验证门控集成
ThinkNet: Compact Architecture Selection and Validation-Gated Ensembles for Subject-Independent MI-EEG Decoding
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中文总结 AI 辅助
ThinkNet提出验证控制的框架，通过仅训练归一化、验证引导搜索和验证门控集成，在受试者无关MI-EEG解码中实现紧凑模型与推理策略的可靠选择，避免性能高估。
中文摘要 AI 辅助
实用的辅助和康复脑机接口需要受试者无关的运动想象脑电图（MI-EEG）解码器，这些解码器能够在目标用户数据有限和计算资源受限的情况下泛化到新用户。然而，当测试受试者的信息影响预处理、模型选择或集成选择时，留出受试者的性能可能被高估。我们提出了ThinkNet，一个验证控制的框架，结合了仅训练归一化、验证引导的进化搜索和验证门控推理，为留出受试者识别紧凑解码器和推理策略。我们评估了四类BCI竞赛IV-2a（会话T）解码，使用九个留一受试者交叉验证（LOSO）折、三个种子、七个固定解码器条目，以及覆盖十个代表性解码器家族的更广泛搜索；留出受试者从未用于归一化、超参数、架构或集成策略选择。在固定基准中，验证选择的紧凑解码器达到了44.35±15.41%的准确率，具有4.9K参数、19 KB FP32权重和0.99 ms批量1 Orin CUDA推理时间。在更广泛的搜索中，紧凑模型（≤25K参数）在选定重训练后实现了比中型和大型替代方案更高的平均留出准确率（40.10%对比35.09%和34.78%）。验证门控集成优于验证选择的单模型推理，在固定基准中达到43.98±16.25%，在紧凑六家族集成中达到43.31±15.88%。一个不可部署的预言机分析揭示了6.1个百分点的家族选择差距和接近零的验证-测试相关性，表明在受试者转移下验证可靠性仍然是一个关键瓶颈。因此，ThinkNet是一个用于紧凑MI-EEG模型和推理策略选择的验证控制框架，而不是单一架构基准。
英文摘要
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 preprocessing, model selection, or ensemble selection. We present \textit{ThinkNet}, a validation-controlled framework that combines train-only normalization, validation-guided evolutionary search, and validation-gated inference to identify compact decoders and inference policies for held-out subjects. We evaluate four-class BCI Competition IV-2a (session T) decoding with nine Leave-One-Subject-Out (LOSO) folds, three seeds, seven fixed decoder entries, and a broader search over ten representative decoder families; the held-out subject is never used for normalization, hyperparameter, architecture, or ensemble-policy selection. In the fixed benchmark, the validation-selected compact decoder achieved 44.35$\pm$15.41\% accuracy with 4.9K parameters, 19 KB FP32 weights, and 0.99 ms batch-1 Orin CUDA inference. Across the broader search, compact models ($\leq$25K parameters) achieved higher mean held-out accuracy than mid-size and large alternatives after selected retraining (40.10\% vs. 35.09\% and 34.78\%). Validation-gated ensembling improved over validation-selected single-model inference, reaching 43.98$\pm$16.25\% in the fixed benchmark and 43.31$\pm$15.88\% for the compact six-family ensemble. A non-deployable oracle analysis revealed a 6.1-point family-selection gap and near-zero validation--test correlation, showing that validation reliability remains a key bottleneck under subject shift. Thus, ThinkNet is a validation-controlled framework for compact MI-EEG model and inference-policy selection, rather than a single-architecture benchmark.
发表机构
- New York University (NYU)（纽约大学）
机构由 AI 辅助整理，请以论文原文为准。
