用于从脑电信号重建语音包络的受试者解耦神经网络
Subject Disentanglement Neural Network for Speech Envelope Reconstruction from EEG
AI总结：
针对脑电信号跨受试者语音包络重建受个体差异影响精度低的问题，提出含三个模块的SDN-Net解耦受试者身份信息，在听觉脑电解码数据集上受试者内和跨任务性能均超现有前沿方法。
摘要：
从EEG（脑电）信号重建语音包络对探索语音感知背后的神经机制至关重要。然而，不同受试者间的EEG变异性以及生理伪影给精准重建带来了困难。为解决该问题，我们提出了Subject Disentangling Neural Network（SDN-Net，受试者解耦神经网络），它将受试者身份信息从重建的语音包络中解耦出来，以提升跨受试者重建的准确率。SDN-Net包含三个核心组件：MLA-Codec、MPN-MI和CTA-MTDNN。其中MLA-Codec是一种全卷积神经网络，负责将EEG信号解码为语音包络；CTA-MTDNN模块是带通道注意力和时间注意力的多尺度时延神经网络，用于从EEG信号中提取受试者身份特征；最后MPN-MI模块是带多层感知机的互信息估计器，用于监督去除重建语音包络中的受试者身份信息。在Auditory EEG Decoding Dataset（听觉脑电解码数据集）上的实验表明，与当前最新的前沿方法相比，SDN-Net在受试者内和跨受试者的语音包络重建任务中均取得了更优的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
