通过置信度感知提升脑电解码的可靠性与鲁棒性。
Confidence-Aware Neural Decoding of Overt Speech from EEG: Toward Robust Brain-Computer Interfaces
- 用深度集成网络结合后处理校准，量化预测不确定性。
- 在多类发音数据上实现更可靠的概率估计和更高选择性表现。
- 适合需要高可信度的脑机接口实际应用，如残障人士沟通。
非侵入式脑机接口需从脑电图（EEG）中准确解码出言说指令，同时保证结果可信。本文提出一种置信度感知的解码框架，结合紧凑的语音导向卷积网络深度集成、事后校准与选择性分类。通过集成预测熵、前两名差距及互信息量化不确定性，决策时引入弃权选项，由准确率-覆盖率权衡点控制。在多类显性发音数据集上评估，采用无泄漏、块分层划分并保留时间连续性。相比广泛使用的基线方法，该方法提供更可靠的概率估计，在不同操作点下提升选择性性能，并实现各类别接受率均衡。结果表明，置信度感知神经解码可为真实世界脑机接口通信系统提供稳健、可部署的行为支持。
原文摘要 · Abstract (English)
Non-invasive brain-computer interfaces that decode spoken commands from electroencephalogram must be both accurate and trustworthy. We present a confidence-aware decoding framework that couples deep ensembles of compact, speech-oriented convolutional networks with post-hoc calibration and selective classification. Uncertainty is quantified using ensemble-based predictive entropy, top-two margin, and mutual information, and decisions are made with an abstain option governed by an accuracy-coverage operating point. The approach is evaluated on a multi-class overt speech dataset using a leakage-safe, block-stratified split that respects temporal contiguity. Compared with widely used baselines, the proposed method yields more reliable probability estimates, improved selective performance across operating points, and balanced per-class acceptance. These results suggest that confidence-aware neural decoding can provide robust, deployment-oriented behavior for real-world brain-computer interface communication systems.
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