  The normalization of brain recordings from multiple subjects responding to
the natural stimuli is one of the key challenges in auditory neuroscience. The
objective of this normalization is to transform the brain data in such a way as
to remove the inter-subject redundancies and to boost the component related to
the stimuli. In this paper, we propose a deep learning framework to improve the
correlation of electroencephalography (EEG) data recorded from multiple
subjects engaged in an audio listening task. The proposed model extends the
linear multi-way canonical correlation analysis (CCA) for audio-EEG analysis
using an auto-encoder network with a shared encoder layer. The model is trained
to optimize a combined loss involving correlation and reconstruction. The
experiments are performed on EEG data collected from subjects listening to
natural speech and music. In these experiments, we show that the proposed deep
multi-way CCA (DMCCA) based model significantly improves the correlations over
the linear multi-way CCA approach with absolute improvements of 0.08 and 0.29
in terms of the Pearson correlation values for speech and music tasks
respectively.
