Confidence-Aware Neural Decoding of Overt Speech from EEG: Toward Robust Brain-Computer Interfaces

• URL: http://arxiv.org/abs/2511.07890v1
• Date: Wed, 12 Nov 2025 01:26:31 GMT
• Title: Confidence-Aware Neural Decoding of Overt Speech from EEG: Toward Robust Brain-Computer Interfaces
• Abstract summary: Non-invasive brain-computer interfaces that decode spoken commands from electroencephalogram must be both accurate and trustworthy.<n>We present a confidence-aware decoding framework that couples deep ensembles of compact, speech-oriented convolutional networks with post-hoc calibration and selective classification.<n>Results suggest that confidence-aware neural decoding can provide robust, deployment-oriented behavior for real-world brain-computer interface communication systems.
• Score: 3.5174884177930448
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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