  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.
