2023/05/03 by Zhenyu Piao, Miseul Kim, Piao, Zhenyu +5 · 1 citation
Computer Science · Neuroscience · #Audio and Speech Processing (eess.AS) #Blind Source Separation Techniques #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #FOS: Electrical engineering #Sound (cs.SD) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2305.06806
openalex publication_date 2023/05/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.