通过循环训练与少量校准，提升脑机接口跨人泛化能力。
Subject-Independent Imagined Speech Detection via Cross-Subject Generalization and Calibration
- 采用循环跨人训练，交替短时训练不同受试者数据。
- 仅用10%目标数据校准，准确率达78.1%，AUC达80.1%。
- 适合需要快速适配新用户的脑机接口系统开发。
基于脑电图的想象言语解码在跨个体泛化方面仍面临重大挑战，主要源于神经活动模式的显著差异。本研究探讨了训练动态与轻量级个体适应对神经解码框架中跨人性能的影响。采用周期性跨人训练方法，即较短的每受试者训练片段与频繁的受试者轮换，实现了在未见目标数据上的适度但一致的性能提升。此外，在受试者校准的留一法设置下，仅需10%的目标受试者数据进行校准，即可达到78.1%的准确率和80.1%的AUC，证明了少样本适应的有效性。这些结果表明，将循环训练与最小校准相结合，是一种简单而有效的策略，可用于构建可扩展、用户自适应的脑机接口系统，兼顾泛化与个性化。
原文摘要 · Abstract (English)
Achieving robust generalization across individuals remains a major challenge in electroencephalogram based imagined speech decoding due to substantial variability in neural activity patterns. This study examined how training dynamics and lightweight subject specific adaptation influence cross subject performance in a neural decoding framework. A cyclic inter subject training approach, involving shorter per subject training segments and frequent alternation among subjects, led to modest yet consistent improvements in decoding performance across unseen target data. Furthermore, under the subject calibrated leave one subject out scheme, incorporating only 10 % of the target subjects data for calibration achieved an accuracy of 0.781 and an AUC of 0.801, demonstrating the effectiveness of few shot adaptation. These findings suggest that integrating cyclic training with minimal calibration provides a simple and effective strategy for developing scalable, user adaptive brain computer interface systems that balance generalization and personalization.
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