用脑电与语音对比学习，实现中文发音意图解码。
SACM: SEEG-Audio Contrastive Matching for Chinese Speech Decoding
- 基于脑电与语音的对比学习框架，直接解码中文发音意图。
- 在8名患者上实现显著高于随机水平的识别准确率。
- 单个运动皮层电极性能接近全阵列，利于轻量化设备开发。
构音障碍和失语症会严重损害患者的口语沟通能力。言语解码脑机接口（BCI）可通过将言语意图直接转化为口语词汇，作为言语神经假体提供替代方案。本文报告了一种针对普通话的言语解码BCI实验方案及相应解码算法。从8名难治性癫痫患者中采集了立体脑电图（SEEG）与同步语音数据，他们在进行词级朗读任务时完成记录。提出的SEEG与音频对比匹配（SACM）是一种基于对比学习的框架，在言语检测与解码任务中均达到显著高于随机水平的准确率。电极层面分析显示，单个运动皮层电极的表现可媲美全电极阵列。这些发现为开发更精确的在线言语解码BCI提供了重要参考。
原文摘要 · Abstract (English)
Speech disorders such as dysarthria and anarthria can severely impair the patient's ability to communicate verbally. Speech decoding brain-computer interfaces (BCIs) offer a potential alternative by directly translating speech intentions into spoken words, serving as speech neuroprostheses. This paper reports an experimental protocol for Mandarin Chinese speech decoding BCIs, along with the corresponding decoding algorithms. Stereo-electroencephalography (SEEG) and synchronized audio data were collected from eight drug-resistant epilepsy patients as they conducted a word-level reading task. The proposed SEEG and Audio Contrastive Matching (SACM), a contrastive learning-based framework, achieved decoding accuracies significantly exceeding chance levels in both speech detection and speech decoding tasks. Electrode-wise analysis revealed that a single sensorimotor cortex electrode achieved performance comparable to that of the full electrode array. These findings provide valuable insights for developing more accurate online speech decoding BCIs.
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