This report describes our submission to Task 2 of the Auditory EEG Decoding Challenge at ICASSP 2023 Signal Processing Grand Challenge (SPGC). Task 2 is a regression problem that focuses on reconstructing a speech envelope from an EEG signal. For the task, we propose a pre-layer normalized feed-forward transformer (FFT) architecture. For within-subjects generation, we additionally utilize an auxiliary global conditioner which provides our model with additional information about seen individuals. Experimental results show that our proposed method outperforms the VLAAI baseline and all other submitted systems. Notably, it demonstrates significant improvements on the within-subjects task, likely thanks to our use of the auxiliary global conditioner. In terms of evaluation metrics set by the challenge, we obtain Pearson correlation values of 0.1895 0.0869 for the within-subjects generation test and 0.0976 0.0444 for the heldout-subjects test. We release the training code for our model online.

翻译：本报告描述了我们参加ICASSP 2023信号处理大挑战赛（SPGC）听觉脑电图解码挑战赛任务2的提交方案。任务2是一个回归问题，旨在从脑电图信号中重建语音包络。为此，我们提出了一种采用预层归一化的前馈Transformer（FFT）架构。在受试者内生成任务中，我们额外引入了一个全局条件模块，该模块为模型提供关于已见个体的附加信息。实验结果表明，我们提出的方法优于VLAAI基线模型及其他所有提交系统。值得注意的是，该方法在受试者内任务中展现出显著改进，这很可能得益于我们使用的全局条件模块。根据挑战赛设定的评估指标，我们在受试者内生成测试中获得了0.1895 ± 0.0869的皮尔逊相关系数，在留出受试者测试中获得了0.0976 ± 0.0444的皮尔逊相关系数。我们已在网上发布了该模型的训练代码。