分离个体差异，提升脑电语音重建准确率
Subject Disentanglement Neural Network for Speech Envelope Reconstruction from EEG
- 设计解耦网络，分离受试者身份信息
- 跨被试重建性能优于现有方法
- 适合脑机接口与神经语言研究
从脑电（EEG）信号中重构语音包络对于理解语音感知的神经机制至关重要。然而，受试者间的脑电差异和生理噪声显著影响重建精度。为此，本文提出受试者解耦神经网络（SDN-Net），通过解耦重建语音包络中的受试者身份信息，提升跨被试重建性能。SDN-Net包含三个核心模块：MLA-Codec为全卷积网络，将脑电信号解码为语音包络；CTA-MTDNN为多尺度时延神经网络，结合通道与时间注意力，提取受试者身份特征；MPN-MI为基于多层感知机的互信息估计器，监督去除重建语音包络中的受试者身份信息。在语音脑电解码数据集上的实验表明，与当前先进方法相比，SDN-Net在同被试与跨被试语音包络重建任务中均取得更优表现。
原文摘要 · Abstract (English)
Reconstructing speech envelopes from EEG signals is essential for exploring neural mechanisms underlying speech perception. Yet, EEG variability across subjects and physiological artifacts complicate accurate reconstruction. To address this problem, we introduce Subject Disentangling Neural Network (SDN-Net), which disentangles subject identity information from reconstructed speech envelopes to enhance cross-subject reconstruction accuracy. SDN-Net integrates three key components: MLA-Codec, MPN-MI, and CTA-MTDNN. The MLA-Codec, a fully convolutional neural network, decodes EEG signals into speech envelopes. The CTA-MTDNN module, a multi-scale time-delay neural network with channel and temporal attention, extracts subject identity features from EEG signals. Lastly, the MPN-MI module, a mutual information estimator with a multi-layer perceptron, supervises the removal of subject identity information from the reconstructed speech envelope. Experiments on the Auditory EEG Decoding Dataset demonstrate that SDN-Net achieves superior performance in inner- and cross-subject speech envelope reconstruction compared to recent state-of-the-art methods.
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